muramed_vgg.ipynb

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    "## MuraMed: A Revolution in Medical Diagnostics\n",
    "---\n",
    "\n",
    "**MuraMed** is an innovative healthcare technology company that aims to revolutionize medical diagnostics with a focus on radiographs (X-Ray images). In the intricate landscape of today's healthcare, the company addresses the vital need for accurate, efficient, and swift diagnosis. MuraMed's specialization lies in detecting bone abnormalities, and it offers an AI-powered diagnostic system tailored for healthcare professionals. This initiative is particularly significant for radiology, a field often challenged by the timeliness and precision of X-ray image interpretation. A brief description of our **MuraMed's Business Case** follows below.\n",
    "\n",
    "In the domain of medical diagnostics, particularly in the field of **bone abnormality detection**, MuraMed has established a unique presence for itself. Using advanced AI technologies, MuraMed has developed a diagnostic system that is highly effective. This system doesn't just represent a technological advancement; it signifies a shift in how musculoskeletal conditions are diagnosed, making it faster and more accurate. This initiative has the potential to improve patient outcomes and overall well-being significantly for those with musculoskeletal disorders.\n",
    "\n",
    "However, **MuraMed**'s expertise extends beyond bone abnormality detection; it also encompasses a broader revolution in **radiology**. The company is at the forefront of integrating Artificial Intelligence into this critical medical field, offering solutions that address the challenges faced by healthcare providers. The AI-driven approach adopted by MuraMed reduces the inefficiencies and human errors associated with traditional diagnostic methods. By providing radiologists and orthopedic doctors with user-friendly AI tools, MuraMed ensures that healthcare providers can make quick, well-informed decisions, ultimately enhancing patient care.\n",
    "\n",
    "The **versatility** of MuraMed's technological platform is another key feature. The application is designed not only to meet the needs of experienced medical professionals but also to extend its benefits to a wider audience, including athletes, teachers, and physiotherapists. These individuals can use this platform for early detection and intervention of musculoskeletal issues. This inclusive approach democratizes diagnostic capabilities, making proactive healthcare more accessible. Through this multifaceted approach, **MuraMed** is set to make a lasting impact across various sectors, from specialized medicine to community healthcare and beyond.\n",
    "\n",
    "\n",
    "\n",
    "<div style=\"text-align: center;\">\n",
    "  <figure>\n",
    "    <img src=\"./Figures/muramed_logo.png\" alt=\"MuraMed Logo\" style=\"width:35%\">\n",
    "    <figcaption>MuraMed: A Revolution in Medical Diagnostics</figcaption>\n",
    "  </figure>\n",
    "</div>\n",
    "\n",
    "\n",
    "When it comes to **MuraMed**'s long-term goals, the company's **Mission and Vision** serve as guiding principles. The company is not solely focused on providing AI-powered diagnostic solutions; it has a broader vision of impacting global healthcare. Specifically, MuraMed aims to ensure that quality healthcare is available to areas facing a severe shortage of skilled radiologists. This vision is not restricted to large healthcare facilities but extends to smaller clinics and even beyond the boundaries of the traditional healthcare industry. By integrating advanced AI algorithms with a specialized focus on bone abnormalities and a versatile application range, MuraMed aims to revolutionize medical diagnostics in an unprecedented manner. Through these initiatives, the company is set to bring about monumental changes that will redefine the landscape of healthcare, making quality diagnostic services accessible and affordable to all.\n",
    "\n",
    "Similarly, the inherent **Value of MuraMed's Research** cannot be overstated. The company's research initiatives are designed to make substantial contributions across multiple domains, including healthcare, technology, business, and innovation. This multi-disciplinary approach not only highlights the company's commitment to improving patient care but also signals its intent to push the boundaries of existing technology. **MuraMed** is invested in creating a harmonious blend of technology and healthcare, using AI as a tool to bridge gaps and address inefficiencies in the current medical systems. Consequently, the company's research serves as a catalyst for business growth, as it opens up new opportunities for expansion and partnerships.\n",
    "\n",
    "In summary, both the mission and the research values of **MuraMed** underscore its commitment to being more than just a healthcare technology company. It strives to be a change-maker in the industry, leveraging technology to address crucial gaps in healthcare accessibility and quality. By doing so, MuraMed aims to foster not just business growth but also societal advancement, setting new standards in healthcare, technology, and innovation.\n",
    "\n",
    "---\n",
    "\n",
    "For further insights into **MuraMed**'s groundbreaking work, one can refer to the comprehensive document: *[MuraMed_Documentation.pdf](./MuraMed_Documentation.pdf)*.\n",
    "\n",
    "While the current analysis will be done on [Jupyter Notebook](http://jupyter.org/) and in [Python 3.10.0](https://www.python.org/downloads/release/python-3100/).\n",
    "\n",
    "---\n",
    "\n",
    "####  Team members:\n",
    "\n",
    "---\n",
    "> Dimitra Diamanti <br />\n",
    "> Academic ID: f2822209 <br />\n",
    "> MSc Business Analytics 2022-2023 FT <br />\n",
    "> Athens University of Economics and Business <br />\n",
    "> dtdiamanti@gmail.com, dim.diamanti@aueb.gr\n",
    "\n",
    "---\n",
    "\n",
    "> Dimitrios Matsanganis <br />\n",
    "> Academic ID: f2822212 <br />\n",
    "> MSc Business Analytics 2022-2023 FT <br />\n",
    "> Athens University of Economics and Business <br />\n",
    "> dmatsanganis@gmail.com, dim.matsanganis@aueb.gr\n",
    "\n",
    "---\n",
    "\n",
    "> Foteini Nefeli Nouskali <br />\n",
    "> Academic ID: f2822213 <br />\n",
    "> MSc Business Analytics 2022-2023 FT <br />\n",
    "> Athens University of Economics and Business <br />\n",
    "> fn.nouskali@gmail.com, fot.nouskali@aueb.gr\n",
    "\n",
    "---\n",
    "\n",
    "> Hegla Eva Ruci <br />\n",
    "> Academic ID: f2822219 <br />\n",
    "> MSc Business Analytics 2022-2023 FT <br />\n",
    "> Athens University of Economics and Business <br />\n",
    "> eva.14rou@gmail.com, heg.ruci@aueb.gr\n",
    "---"
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    "### Detailed Overview of MuraMed's Business Case\n",
    "\n",
    "---\n",
    "\n",
    "As mentioned briefly above, **MuraMed's Business Case** is a comprehensive blueprint that outlines the company's strategic approach to revolutionize medical diagnostics, specifically focusing on musculoskeletal radiographs. By leveraging advanced AI and deep learning technologies, MuraMed aims to address critical gaps in the healthcare, sports, and workplace sectors. The business case is built around three core pillars, each designed to cater to the unique needs of different demographic groups and industries.\n",
    "\n",
    "\n",
    "#### The Three Core Pillars of MuraMed's Business Case\n",
    "\n",
    "1. **MuraMed: Healthcare Edition**\n",
    "    - This pillar serves as the cornerstone of MuraMed's business model, targeting traditional healthcare facilities like hospitals and clinics. By focusing on specific areas like the hand, elbow, and shoulder, it aims to bring unparalleled precision to musculoskeletal radiograph analysis. The platform's AI-backed second opinion feature is particularly beneficial for radiologists and orthopedic doctors, enhancing diagnostic accuracy and efficiency. Furthermore, its seamless integration capabilities with existing hospital systems make it an invaluable asset in accelerating diagnostic services.\n",
    "\n",
    "\n",
    "2. **MuraMed: School & Sports Organization Edition**\n",
    "    - Tailored for the unique needs of young athletes and students, this pillar extends MuraMed's reach beyond conventional healthcare settings. By offering real-time detection of musculoskeletal abnormalities, it plays a transformative role in sports medicine. The platform is designed to be versatile, applicable in sports activities where hand injuries are common, such as basketball, volleyball, and handball. With its potential to positively impact the health and performance of young athletes, this pillar significantly broadens MuraMed's market reach.\n",
    "\n",
    "\n",
    "3. **MuraMed: Workplace Edition**\n",
    "    - Addressing the growing concern of work-related musculoskeletal disorders (MSDs), this pillar is designed for a broad range of industries. From manual labor-intensive sectors to office-based jobs, it aims to tackle the unique challenges each profession faces concerning MSDs. The platform offers regular employee check-ups and ergonomic evaluations as preventive measures. Supported by alarming statistics from the U.S. Department of Labor, this pillar emphasizes the need for early detection and management of hand and upper limb conditions, thereby promoting healthier workplaces.\n",
    "\n",
    "---\n",
    "\n",
    "#### Strategic Importance\n",
    "\n",
    "The strategic alignment of these three pillars enables MuraMed to offer specialized solutions that address the nuanced needs of various sectors. From healthcare professionals requiring precise and rapid diagnostics to sports organizations and workplaces seeking to enhance well-being and performance, MuraMed's comprehensive business case sets a new benchmark in the realm of musculoskeletal health diagnostics.\n",
    "\n",
    "By integrating cutting-edge AI technologies with sector-specific requirements, MuraMed's business case presents a multifaceted approach to improve diagnostic accuracy, efficiency, and accessibility across different verticals. It not only targets the immediate needs of these sectors but also positions the company for future growth and innovation in the rapidly evolving landscape of medical diagnostics."
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    "---\n",
    "\n",
    "### Mura Dataset\n",
    "\n",
    "At the core of **MuraMed**'s capabilities is its reliance on extensive datasets provided by Stanford University, known as the MURA datasets (combine for different parts of the human body). These datasets are a rich library of manually reviewed and labeled musculoskeletal radiographs, focusing on multiple body parts such as the wrist, shoulder, elbow, and more. The datasets form the backbone of MuraMed's AI algorithms, enabling them to accurately identify normal or abnormal characteristics in X-ray studies. More information about the MURA dataset can be found at [Stanford's ML site](https://stanfordmlgroup.github.io/competitions/mura/) or at [Kaggle](https://www.kaggle.com/datasets/cjinny/mura-v11/download?datasetVersionNumber=1).\n",
    "\n",
    "<br>\n",
    "<br>\n",
    "<div style=\"text-align: center;\">\n",
    "  <figure>\n",
    "    <img src=\"./Figures/muramed1.png\" alt=\"Official Stanford ML Logo\" style=\"width:30%\">\n",
    "    <figcaption>Figure 1. Official Stanford ML Logo</figcaption>\n",
    "  </figure>\n",
    "</div>\n",
    "<br>\n",
    "\n",
    "Currently, our algorithms are designed to *focus on the aforementioned body parts*. Their primary task is to determine whether an X-ray study of these areas exhibits normal or abnormal characteristics. In the near future, we plan to expand our AI capabilities to involve a broader spectrum of anatomical regions , further enhancing our diagnostic capabilities.\n",
    "\n",
    "<br><br>\n",
    "<div style=\"text-align: center;\">\n",
    "  <figure>\n",
    "    <img src=\"./Figures/muramed2.jpg\" alt=\"A detailed image showcasing the anatomical parts included in the Mura Dataset\" style=\"width:20%\">\n",
    "    <figcaption>Figure 2. A detailed image showcasing the anatomical parts included in the Mura Dataset</figcaption>\n",
    "  </figure>\n",
    "</div>"
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    "### Transitioning from Business Vision to Technical Implementation - Roadmap\n",
    "---\n",
    "Having presented the core objectives and innovative prospects of our business idea, it is essential to move on to the technical implementation phase. To ensure clarity, smooth execution, and enhance stakeholder understanding, we present the following roadmap.\n",
    "\n",
    "\n",
    "### **Table of Contents**\n",
    "[1. Libraries and Installation Guidelines](#libraries-and-installation-guidelines) <br>\n",
    "[2. MURA Dataset](#mura-dataset) <br>\n",
    "[3. Data Loading and Feature Engineering Transformation Workflow](#data-loading-and-feature-engineering-transformation-workflow) <br>\n",
    "[4. Exploring Distribution of Normal and Abnormal Case Studies Across Anatomical Body Parts in MURA Dataset](#exploring-distribution-of-normal-and-abnormal-case-studies-across-anatomical-body-parts-in-mura-dataset) <br>\n",
    "[5. Data Splitting Training and Validation Sets](#data-splitting-training-and-validation-sets) <br>\n",
    "[6. Analyzing Case Study Distribution for CNN Implementation in the MURA Dataset](#analyzing-case-study-distribution-for-cnn-implementation-in-the-mura-dataset) <br>\n",
    "[7. Image Data Generation and Augmentation](#image-data-generation-and-augmentation) <br>\n",
    "[8. Building a VGG19 - Based Binary Classification Model](#building-a-vgg19---based-binary-classification-model) <br>\n",
    "[9. VGG-19-Based Model Training Procedure](#vgg-19-based-model-training-procedure) <br>\n",
    "[10. Training Phases in VGG-19-Based Model: Upper Layers and Full Network](#training-phases-in-vgg-19-based-model:-upper-layers-and-full-network) <br>\n",
    "[11. VGG-19 Model Summary Interpretation](#vgg-19-model-summary-interpretation) <br>\n",
    "[12. Visualization through Plot Model](#visualization-through-plot-model) <br>\n",
    "[13. VGG-Model Evaluation](#vgg-model-evaluation) <br>\n",
    "[14. Model Evaluation and Learning Curves](#model-evaluation-and-learning-curves) <br>\n",
    "[15. VGG Model Evaluation and Performance Plotting](#vgg-model-evaluation-and-performance-plotting) <br>\n",
    "[16. VGG Model Training Insights](#vgg-model-training-insights) <br>\n",
    "[17. VGG Model Evaluation on Test Data](#vgg-model-evaluation-on-test-data) <br>\n",
    "[18. VGG Model Evaluation on Test Data Insights](#vgg-model-evaluation-on-test-data-insights) <br>\n",
    "[19. Evaluating Model Performance Across Different Study Types](#evaluating-model-performance-across-different-study-types) <br>\n",
    "[20. Evaluating Model Performance Across Different Study Types Insights](#evaluating-model-performance-across-different-study-types-insights) <br>\n",
    "[21. VGG Model Evaluation with Best Epoch Weights](#vgg-model-evaluation-with-best-epoch-weights) <br>\n",
    "[22. Evaluating Model Performance (with Best Epoch Weights) Across Different Study Types Insights](#evaluating-model-performance-(with-best-epoch-weights)-across-different-study-types-insights) <br>\n",
    "[23. Conclusion](#conclusion) <br>\n"
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    "### Libraries and Installation Guidelines <a class=\"anchor\" id=\"libraries-and-installation-guidelines\"></a>\n",
    "---\n",
    "\n",
    "In this Jupyter Notebook, we focus on leveraging a robust set of Python libraries tailored for data manipulation, statistical analysis, machine learning, and deep learning. These libraries are integral to the data science pipeline, aiding in various tasks from data preprocessing to model evaluation. Below is an in-depth look at each library's role, along with their respective documentation links for further exploration.\n",
    "\n",
    "- **Pandas**: Employed for data manipulation and analysis, it serves as the initial point for data loading and transformation.\n",
    "  - [Pandas Documentation](https://pandas.pydata.org/docs/)\n",
    "  \n",
    "  \n",
    "- **NumPy**: Essential for numerical operations, it works hand-in-hand with Pandas for data transformation and manipulation.\n",
    "  - [NumPy Documentation](https://numpy.org/doc/)\n",
    "  \n",
    "  \n",
    "- **Matplotlib**: Utilized for data visualization, it aids in plotting graphs and charts to better understand the dataset.\n",
    "  - [Matplotlib Documentation](https://matplotlib.org/stable/contents.html)\n",
    "  \n",
    "  \n",
    "- **Seaborn**: An extension of Matplotlib, used for more complex visualizations, and it offers a higher level of abstraction.\n",
    "  - [Seaborn Documentation](https://seaborn.pydata.org/)\n",
    "  \n",
    "  \n",
    "- **Scikit-learn**: Provides a wide range of machine learning algorithms and is used here for splitting datasets into training and test sets.\n",
    "  - [Scikit-learn Documentation](https://scikit-learn.org/stable/)\n",
    "  \n",
    "  \n",
    "- **TensorFlow**: The backbone for deep learning tasks, it offers a comprehensive and flexible ecosystem for building and deploying machine learning models.\n",
    "  - [TensorFlow Documentation](https://www.tensorflow.org/guide)\n",
    "  \n",
    "  \n",
    "- **TensorFlow Addons**: An additional package that extends TensorFlow functionalities, providing extra layers and metrics.\n",
    "  - [TensorFlow Addons Documentation](https://www.tensorflow.org/addons/overview)\n",
    "  \n",
    "  \n",
    "By integrating these libraries, we aim to build an organized efficient pipeline for developing advanced machine learning models for medical diagnostics as mentioned above."
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      "Pandas version: 2.1.0\n",
      "NumPy version: 1.24.3\n",
      "Matplotlib version: 3.7.2\n",
      "Seaborn version: 0.12.2\n",
      "Scikit-learn version: 1.3.0\n",
      "Tensorflow version: 2.13.0-rc0\n"
     ]
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    "# Utilized Libraries of the current analysis.\n",
    "\n",
    "# Basic Data Manipulation Libraries.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "print(f'Pandas version: {pd.__version__}')\n",
    "print(f'NumPy version: {np.__version__}')\n",
    "\n",
    "# Data Visualization Libraries.\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "print(f'Matplotlib version: {matplotlib.__version__}')\n",
    "print(f'Seaborn version: {sns.__version__}')\n",
    "%matplotlib inline\n",
    "from IPython.display import Image\n",
    "\n",
    "# Utilities.\n",
    "import gc  # Garbage collection.\n",
    "import zipfile  # For handling zip files.\n",
    "import time  # For timing operations.\n",
    "import random  # For generating random numbers.\n",
    "import locale # For dealing with certain cultural issues.\n",
    "\n",
    "# Scikit-learn for Model Preparation.\n",
    "from sklearn.model_selection import train_test_split\n",
    "import sklearn\n",
    "print(f'Scikit-learn version: {sklearn.__version__}')\n",
    "\n",
    "# TensorFlow and Keras for Model Building, Training, and Evaluation.\n",
    "import os\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'  # Suppress TensorFlow logging.\n",
    "\n",
    "# Use the os. environ module to set the TF_CPP_MIN_LOG_LEVEL environment variable to 3 to\n",
    "# disable/suppress all tensorflow warnings.\n",
    "# When the environment variable is set to 3 , info, warning and error messages are not logged.\n",
    "\n",
    "# Suppress specific TensorFlow Addons warnings.\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore', category=UserWarning, module='tensorflow_addons')  # Suppress TFA UserWarnings.\n",
    "\n",
    "from datetime import datetime # Datetime.\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.random import set_seed  # For reproducibility.\n",
    "from tensorflow.keras import backend as K  # Keras backend.\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator  # Data augmentation.\n",
    "from tensorflow.keras.optimizers import Adam  # Optimizer.\n",
    "from tensorflow.keras.applications import VGG19  # VGG19 model.\n",
    "from tensorflow.keras.layers import Input, Conv2D, BatchNormalization, MaxPool2D, Dropout, Flatten, Dense  # Layers.\n",
    "from tensorflow.keras.models import Model  # Model class.\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint  # Callbacks.\n",
    "from tensorflow.keras.applications.vgg19 import preprocess_input  # Preprocessing.\n",
    "from tensorflow.keras.regularizers import l2  # L2 regularization.\n",
    "from tensorflow.keras.utils import plot_model  # For plotting model architecture.\n",
    "\n",
    "# Metrics for Model Evaluation.\n",
    "from tensorflow.keras.metrics import BinaryAccuracy, Precision, Recall, AUC\n",
    "from tensorflow_addons.metrics import CohenKappa\n",
    "\n",
    "# Scikit-learn Metrics for Model Evaluation.\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score, roc_auc_score, cohen_kappa_score\n",
    "\n",
    "print(f'Tensorflow version: {tf.__version__}')"
   ]
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    "Based on the information provided above, the libraries utilized are the following:\n",
    "\n",
    "- **Pandas**: Version 2.1.0\n",
    "- **NumPy**: Version 1.24.3\n",
    "- **Matplotlib**: Version 3.7.2\n",
    "- **Seaborn**: Version 0.12.2\n",
    "- **Scikit-learn**: Version 1.3.0\n",
    "- **TensorFlow**: Version 2.13.0-rc0\n",
    "\n",
    "For a more detailed specification, we can note the following:\n",
    "\n",
    "- **Pandas**: The environment utilizes Pandas version **2.1.0**, which provides robust capabilities for data manipulation and analysis.\n",
    "\n",
    "  \n",
    "- **NumPy**: The version in use is **1.24.3**, essential for a variety of numerical operations. It complements Pandas in data transformation and manipulation.\n",
    "\n",
    "\n",
    "- **Matplotlib**: The current version is **3.7.2**, offering a wide array of functionalities for data visualization.\n",
    "\n",
    "\n",
    "- **Seaborn**: Version **0.12.2** is installed, extending Matplotlib's functionalities for more advanced data visualizations.\n",
    "\n",
    "\n",
    "- **Scikit-learn**: The environment leverages Scikit-learn version **1.3.0**, a comprehensive library for machine learning tasks such as data splitting, model training, and evaluation.\n",
    "\n",
    "\n",
    "- **TensorFlow**: Version **2.13.0-rc0** forms the backbone for all deep learning tasks, offering a comprehensive and flexible platform for building, training, and deploying machine learning models. Furthermore, **TensorFlow** serves as the cornerstone of our deep learning applications. Developed by Google, TensorFlow offers a comprehensive ecosystem for deploying machine learning models, including those based on neural networks. The library is particularly optimized for high-performance numerical computation, a crucial feature for various computational tasks.\n",
    "\n",
    "Overall, these versions indicate a well-updated environment, suitable for our tasks.\n",
    "\n",
    "---\n",
    "\n",
    "**_Important Note 1 - UserWarning:_** You may receive a UserWarning that indicates that TensorFlow Addons (TFA) has stopped development of new features and is in maintenance mode until May 2024. This means it's advisable to transition to other TensorFlow community libraries for new features or functions that were previously covered by TFA (this warning has been suppressed with filterwarnings).\n",
    "\n",
    "For more information see: [GitHub Issue](https://github.com/tensorflow/addons/issues/2807)\n",
    "\n",
    "**_Important Note 2 - TensorFlow and Installation:_** The installation of Tensorflow can sometimes be tricky. To install TensorFlow through `pip`, the following commands should be run:\n",
    "\n",
    "```bash\n",
    "pip install tensorflow\n",
    "```\n",
    "\n",
    "To additionally install TensorFlow Addons:\n",
    "\n",
    "```bash\n",
    "pip install tensorflow-addons\n",
    "```"
   ]
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    "### MURA Dataset <a class=\"anchor\" id=\"mura-dataset\"></a>\n",
    "---\n",
    "\n",
    "The **MURA dataset**, an acronym for *Musculoskeletal Radiographs*, is a large-scale, specialized collection of digital X-ray images that was officially released by **Stanford University** in the year 2017. This dataset is instrumental to the medical community as it consists of an extensive array of over **40,000 digital X-ray images** spanning **seven different anatomical regions**, namely the wrist, elbow, shoulder, finger, hip, knee, and ankle. The **MURA dataset** is structured into two primary folders, namely **\"train\"** and **\"valid\"**, each containing datasets relevant to their respective categories. In total, the MURA dataset comprises approximately **41,000 images** derived from around **15,000 distinct studies** (patients). These images are distributed across both the training and validation sets. The dataset is thoughtfully curated to encompass a wide range of medical conditions, allowing for comprehensive evaluation and training of diagnostic models.\n",
    "\n",
    "Within this dataset, there are **9,000** studies representing **normal** or negative conditions, serving as essential reference points for identifying healthy anatomical structures. Additionally, there are **6,000** studies representing **abnormal** or positive conditions, encompassing a variety of pathological findings that require accurate and timely detection. The typical image resolution in the MURA dataset is 500 x 500 pixels. More information about the MURA dataset can be found at [Stanford's ML site](https://stanfordmlgroup.github.io/competitions/mura/) or at [Kaggle](https://www.kaggle.com/datasets/cjinny/mura-v11/download?datasetVersionNumber=1).\n",
    "\n",
    "<br>\n",
    "<div style=\"text-align: center;\">\n",
    "  <figure>\n",
    "    <img src=\"./Figures/muramed3.png\" alt=\"MURA Dataset Instances\" style=\"width:35%\">\n",
    "    <figcaption>Figure 3. MURA Dataset Instances</figcaption>\n",
    "  </figure>\n",
    "</div>\n",
    "<br>\n",
    "\n",
    "Over the years, the MURA dataset has emerged as a *benchmark standard* for assessing the efficacy of deep learning models in the domain of musculoskeletal radiography. It has been extensively employed in numerous research studies and competitions, serving as the foundation for algorithms designed to detect a wide range of issues including fractures, dislocations, and other anatomical irregularities in X-ray images. This is the reason that makes us decide to get involved with **MuraMed**."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d346328",
   "metadata": {
    "id": "8d346328"
   },
   "source": [
    "#### Downloading the MURA Dataset\n",
    "---\n",
    "\n",
    "To access the MURA dataset for your research or application, you can utilize the following Kaggle link: [Download MURA Dataset](https://www.kaggle.com/datasets/cjinny/mura-v11/download?datasetVersionNumber=1).\n",
    "\n",
    "You can follow these steps:\n",
    "\n",
    "1. Navigate to the dataset page on Kaggle.\n",
    "2. Click on the \"Download\" button. This will download a ZIP file to your computer.\n",
    "3. Upload the ZIP file to your Jupyter Notebook environment.\n",
    "\n",
    "Alternatively, you can download the dataset directly within your Jupyter Notebook (remember to remove the comment):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4f9d0cf1",
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1694843600682,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "4f9d0cf1",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# MURA Dataset download through Kaggle.\n",
    "# !wget \"https://www.kaggle.com/datasets/cjinny/mura-v11/download?datasetVersionNumber=1\" -O MURA-v1.1.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0b5a163",
   "metadata": {
    "id": "f0b5a163"
   },
   "source": [
    "Please note that this command will download the ZIP file and name it `MURA-v1.1.zip`. The `-O` flag specifies the output file name.\n",
    "\n",
    "**_Important Note:_** The direct download link from Kaggle usually requires you to be logged in. Therefore, the above `wget` method might not work without proper authentication or cookies. In such cases, manual download and upload would be the straightforward approach.\n",
    "\n",
    "You can try to download the file through [Stanford's ML site](https://stanfordmlgroup.github.io/competitions/mura/)  To download the dataset you must first fill out a form."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aa431aa0",
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1694843600682,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "aa431aa0"
   },
   "outputs": [],
   "source": [
    "# MURA Dataset download through Stanford ML.\n",
    "# !wget 'https://cs.stanford.edu/group/mlgroup/MURA-v1.1.zip' -O MURA-v1.1.zip"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "facd29f7",
   "metadata": {
    "id": "facd29f7"
   },
   "source": [
    "As mentioned earlier, please be aware that this command will download the ZIP file and save it as `MURA-v1.1.zip`. The `-O` flag is used to define the output file name.\n",
    "\n",
    "**_Important Note:_** It's worth noting that the direct download link from Stanford's ML site may require proper authentication or the use of cookies. In such cases, manually downloading and then uploading the file would be the most straightforward approach."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d26c79c9",
   "metadata": {
    "id": "d26c79c9"
   },
   "source": [
    "#### Unzipping the MURA Dataset\n",
    "---\n",
    "\n",
    "If the `wget` command successfully downloads the ZIP file, you can proceed to unzip it in your Jupyter Notebook environment.  You can use Python's `zipfile` library to extract the contents.\n",
    "\n",
    "Here's how you can do it (remove the comments):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "55d7ccd1",
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1694843600682,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "55d7ccd1",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# import zipfile\n",
    "\n",
    "# with zipfile.ZipFile('MURA-v1.1.zip', 'r') as zip_ref:\n",
    "#     zip_ref.extractall('MURA-v1.1')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29f71bd3",
   "metadata": {
    "id": "29f71bd3"
   },
   "source": [
    "This code will extract all the files from `MURA-v1.1.zip` into a directory named `MURA-v1.1`.\n",
    "\n",
    "After the dataset is successfully unzipped, you can proceed with the notebook as per the roadmap outlines."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31d8ba6a",
   "metadata": {
    "id": "31d8ba6a"
   },
   "source": [
    "### Setting the Seed for Reproducibility\n",
    "---\n",
    "\n",
    "In order to ensure that the experiments are reproducible, it is essential to set a seed for both NumPy and TensorFlow. By setting a seed, we ensure that the random numbers generated by these libraries are the same in every run. This is crucial for debugging and comparing models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8964856f",
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1694843600682,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "8964856f"
   },
   "outputs": [],
   "source": [
    "# Seed value for reproducibility.\n",
    "seed = 1001101\n",
    "\n",
    "# Setting the seed for NumPy.\n",
    "np.random.seed(seed)\n",
    "\n",
    "# Setting the seed for TensorFlow.\n",
    "set_seed(seed)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58d883c2",
   "metadata": {
    "id": "58d883c2"
   },
   "source": [
    "By setting the seed value to *`1001101` (01001101 = Capital M in binary format)*, we ensure that the random processes in both NumPy and TensorFlow libraries will produce the same set of random numbers, thereby making the experiments reproducible."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99ef3763",
   "metadata": {
    "id": "99ef3763"
   },
   "source": [
    "### Dataset Validation\n",
    "---\n",
    "The code is carefully structured to **validate the availability** of the **MURA-v1.1 dataset** along with its relevant train and test CSV files within a specified directory. Initially, the code sets the filepath where it expects to find the dataset. After confirming the directory's existence, it then constructs the full filepaths for both the **train** and **test CSV files** (*train_image_paths.csv* and *valid_image_paths.csv*, respectively). Finally, it assesses the existence of these critical files. If any element is missing at any stage, an error message is promptly displayed. This initial step is crucial as it ensures that all necessary files and directories are prepared, thus enabling a smooth execution of subsequent data loading and preprocessing tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4de57e35",
   "metadata": {
    "executionInfo": {
     "elapsed": 27,
     "status": "ok",
     "timestamp": 1694843773221,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "4de57e35"
   },
   "outputs": [],
   "source": [
    "# Initialize the filepath for the MURA-v1.1 dataset.\n",
    "filepath = 'MURA-v1.1'\n",
    "\n",
    "# Function to check the existence of a file or directory.\n",
    "def check_path_exists(path, path_type=\"Directory\"):\n",
    "    if not os.path.exists(path):\n",
    "        print(f\"Error: {path_type} {path} does not exist.\")\n",
    "        return False\n",
    "    return True\n",
    "\n",
    "# Check if the main dataset directory exists.\n",
    "if check_path_exists(filepath):\n",
    "\n",
    "    # Construct the filepaths for the train and test set CSV files.\n",
    "    train_set_filepath = os.path.join(filepath, 'train_image_paths.csv')\n",
    "    test_set_filepath = os.path.join(filepath, 'valid_image_paths.csv')\n",
    "\n",
    "    # Check if the train and test CSV files exist.\n",
    "    check_path_exists(train_set_filepath, \"Train set file\")\n",
    "    check_path_exists(test_set_filepath, \"Test set file\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5acec943",
   "metadata": {
    "id": "5acec943"
   },
   "source": [
    "### Data Loading and Feature Engineering Transformation Workflow <a class=\"anchor\" id=\"data-loading-and-feature-engineering-transformation-workflow\"></a>\n",
    "---\n",
    "\n",
    "In the technical pipeline of **MuraMed**, an essential component is the function `load_and_transform_data`, designed to perform multiple tasks beyond merely loading the data into a DataFrame. This function serves as a **comprehensive tool** for ingesting the raw data and transforming it into a structured, feature-rich format suitable for further analysis and model training. Here's a detailed breakdown of the operations carried out by this function.\n",
    "\n",
    "1. **Loading Data Into a DataFrame**: The function begins by reading the CSV file specified by the `filepath` argument. It loads this data into a Pandas DataFrame with a single column, `image_path`, which contains the paths to the individual image files.\n",
    "\n",
    "\n",
    "2. **Generating Descriptions**: The `description` column is generated by slicing and concatenating specific segments of the `image_path`. TThis new column serves as a concise representation of each image's metadata, summarizing information about the study type, patient code, and the study's medical opinion (positive or negative).\n",
    "\n",
    "\n",
    "3. **Study Type Extraction**: The `type` column is derived from the `description` column and holds the type of study (e.g., elbow, shoulder, etc.). This information is crucial for any stratified analyses or specialized models that focus on particular types of studies.\n",
    "\n",
    "\n",
    "4. **Patient Code Extraction**: The `patient` column is also extracted from the `description` and contains unique patient identifiers. This could be valuable for long-term analyses or for models that consider patient-specific characteristics.\n",
    "\n",
    "\n",
    "5. **Study Code Extraction**: The `study` column, another part of the `description`, captures the specific study code. TThis enables easy grouping images belonging to the same study, which can be useful in multi-image analyses.\n",
    "\n",
    "\n",
    "6. **Medical Opinion Extraction**: The `opinion` column holds the medical opinion of each study, extracted from the `description`. This is a binary attribute indicating whether the study was considered positive or negative, and it serves as the foundation for our target variable.\n",
    "\n",
    "\n",
    "7. **Label Generation**: Finally, the function creates the `label` column, which is a binary representation of the `opinion` column. Here, positive opinions are encoded as 1, and negative opinions are encoded as 0. This column serves as the target variable for our machine learning models.\n",
    "\n",
    "This function, therefore, does more than just load data;  it carries out detailed feature engineering to extract valuable information from raw data paths. These attributes are then organized into a Data Frame, transforming unstructured data into a format suitable for machine learning.\n",
    "\n",
    "To sum up, the `load_and_transform_data` function is a cornerstone in **MuraMed's** data pipeline, offering a streamlined and efficient method for data ingestion and preparation. It ensures that the loaded data is not only stored efficiently but is also enriched with features that are critical ffor subsequent tasks, such as data visualization, statistical analysis, and machine learning model training. By automating these tasks into a single function, **MuraMed** significantly enhances workflow efficiency, enabling a more focused approach to model development and evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "77160ccb",
   "metadata": {
    "executionInfo": {
     "elapsed": 27,
     "status": "ok",
     "timestamp": 1694843773222,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "77160ccb"
   },
   "outputs": [],
   "source": [
    "def load_and_transform_data(filepath: str):\n",
    "\n",
    "    # Load the raw data from the CSV file into a Pandas DataFrame.\n",
    "    df = pd.read_csv(filepath, header=None, names=['image_path'])\n",
    "\n",
    "    # Generate a 'description' column by slicing and joining segments from the 'image_path'.\n",
    "    # The description serves as a compact representation of each study's metadata.\n",
    "    df['description'] = df.image_path.apply(lambda x: '_'.join(x.split('/')[2:5])[3:])\n",
    "\n",
    "    # Extract the type of medical study (e.g., elbow, shoulder, etc.) and store it in the 'type' column.\n",
    "    df['type'] = df.description.apply(lambda x: x.split('_')[0])\n",
    "\n",
    "    # Extract the unique patient identifier for each study and store it in the 'patient' column.\n",
    "    df['patient'] = df.description.apply(lambda x: x.split('_')[1])\n",
    "\n",
    "    # Extract the specific study code for each image and store it in the 'study' column.\n",
    "    # This enables easy grouping of images belonging to the same study.\n",
    "    df['study'] = df.description.apply(lambda x: x.split('_')[2])\n",
    "\n",
    "    # Extract the medical opinion (positive or negative) for each study and store it in the 'opinion' column.\n",
    "    df['opinion'] = df.description.apply(lambda x: x.split('_')[3])\n",
    "\n",
    "    # Create the 'label' column, which will serve as the target variable for machine learning models.\n",
    "    # Positive opinions are encoded as 1 and negative opinions as 0.\n",
    "    df['label'] = np.where(df.opinion == 'positive', 1, 0)\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "802c42a5",
   "metadata": {
    "id": "802c42a5"
   },
   "source": [
    "### Presenting the Datasets\n",
    "---\n",
    "\n",
    "In this section, we delve into the **training** and **test** datasets that will be used for building our models. The primary focus is to understand the shape, characteristics, and unique features of these datasets. This will be crucial for downstream tasks such as model training and evaluation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb1e0224",
   "metadata": {
    "id": "fb1e0224"
   },
   "source": [
    "#### Loading and Transforming the Data\n",
    "---\n",
    "\n",
    "Initially, we use a custom function `load_and_transform_data` to load the datasets from their respective file paths. This function handles necessary preprocessing steps like feature extraction and label encoding. Below we will present the datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "93d6c52d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 380
    },
    "executionInfo": {
     "elapsed": 26,
     "status": "ok",
     "timestamp": 1694843773222,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "93d6c52d",
    "outputId": "bfe890d4-4fc3-4cf1-900e-5cc77abc1f3c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First few rows of the training set:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_path</th>\n",
       "      <th>description</th>\n",
       "      <th>type</th>\n",
       "      <th>patient</th>\n",
       "      <th>study</th>\n",
       "      <th>opinion</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MURA-v1.1/train/XR_SHOULDER/patient00001/study...</td>\n",
       "      <td>SHOULDER_patient00001_study1_positive</td>\n",
       "      <td>SHOULDER</td>\n",
       "      <td>patient00001</td>\n",
       "      <td>study1</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MURA-v1.1/train/XR_SHOULDER/patient00001/study...</td>\n",
       "      <td>SHOULDER_patient00001_study1_positive</td>\n",
       "      <td>SHOULDER</td>\n",
       "      <td>patient00001</td>\n",
       "      <td>study1</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MURA-v1.1/train/XR_SHOULDER/patient00001/study...</td>\n",
       "      <td>SHOULDER_patient00001_study1_positive</td>\n",
       "      <td>SHOULDER</td>\n",
       "      <td>patient00001</td>\n",
       "      <td>study1</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MURA-v1.1/train/XR_SHOULDER/patient00002/study...</td>\n",
       "      <td>SHOULDER_patient00002_study1_positive</td>\n",
       "      <td>SHOULDER</td>\n",
       "      <td>patient00002</td>\n",
       "      <td>study1</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MURA-v1.1/train/XR_SHOULDER/patient00002/study...</td>\n",
       "      <td>SHOULDER_patient00002_study1_positive</td>\n",
       "      <td>SHOULDER</td>\n",
       "      <td>patient00002</td>\n",
       "      <td>study1</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MURA-v1.1/train/XR_SHOULDER/patient00002/study...</td>\n",
       "      <td>SHOULDER_patient00002_study1_positive</td>\n",
       "      <td>SHOULDER</td>\n",
       "      <td>patient00002</td>\n",
       "      <td>study1</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MURA-v1.1/train/XR_SHOULDER/patient00003/study...</td>\n",
       "      <td>SHOULDER_patient00003_study1_positive</td>\n",
       "      <td>SHOULDER</td>\n",
       "      <td>patient00003</td>\n",
       "      <td>study1</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MURA-v1.1/train/XR_SHOULDER/patient00003/study...</td>\n",
       "      <td>SHOULDER_patient00003_study1_positive</td>\n",
       "      <td>SHOULDER</td>\n",
       "      <td>patient00003</td>\n",
       "      <td>study1</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MURA-v1.1/train/XR_SHOULDER/patient00003/study...</td>\n",
       "      <td>SHOULDER_patient00003_study1_positive</td>\n",
       "      <td>SHOULDER</td>\n",
       "      <td>patient00003</td>\n",
       "      <td>study1</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MURA-v1.1/train/XR_SHOULDER/patient00004/study...</td>\n",
       "      <td>SHOULDER_patient00004_study1_positive</td>\n",
       "      <td>SHOULDER</td>\n",
       "      <td>patient00004</td>\n",
       "      <td>study1</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          image_path  \\\n",
       "0  MURA-v1.1/train/XR_SHOULDER/patient00001/study...   \n",
       "1  MURA-v1.1/train/XR_SHOULDER/patient00001/study...   \n",
       "2  MURA-v1.1/train/XR_SHOULDER/patient00001/study...   \n",
       "3  MURA-v1.1/train/XR_SHOULDER/patient00002/study...   \n",
       "4  MURA-v1.1/train/XR_SHOULDER/patient00002/study...   \n",
       "5  MURA-v1.1/train/XR_SHOULDER/patient00002/study...   \n",
       "6  MURA-v1.1/train/XR_SHOULDER/patient00003/study...   \n",
       "7  MURA-v1.1/train/XR_SHOULDER/patient00003/study...   \n",
       "8  MURA-v1.1/train/XR_SHOULDER/patient00003/study...   \n",
       "9  MURA-v1.1/train/XR_SHOULDER/patient00004/study...   \n",
       "\n",
       "                             description      type       patient   study  \\\n",
       "0  SHOULDER_patient00001_study1_positive  SHOULDER  patient00001  study1   \n",
       "1  SHOULDER_patient00001_study1_positive  SHOULDER  patient00001  study1   \n",
       "2  SHOULDER_patient00001_study1_positive  SHOULDER  patient00001  study1   \n",
       "3  SHOULDER_patient00002_study1_positive  SHOULDER  patient00002  study1   \n",
       "4  SHOULDER_patient00002_study1_positive  SHOULDER  patient00002  study1   \n",
       "5  SHOULDER_patient00002_study1_positive  SHOULDER  patient00002  study1   \n",
       "6  SHOULDER_patient00003_study1_positive  SHOULDER  patient00003  study1   \n",
       "7  SHOULDER_patient00003_study1_positive  SHOULDER  patient00003  study1   \n",
       "8  SHOULDER_patient00003_study1_positive  SHOULDER  patient00003  study1   \n",
       "9  SHOULDER_patient00004_study1_positive  SHOULDER  patient00004  study1   \n",
       "\n",
       "    opinion  label  \n",
       "0  positive      1  \n",
       "1  positive      1  \n",
       "2  positive      1  \n",
       "3  positive      1  \n",
       "4  positive      1  \n",
       "5  positive      1  \n",
       "6  positive      1  \n",
       "7  positive      1  \n",
       "8  positive      1  \n",
       "9  positive      1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the training data using our custom function.\n",
    "train_set = load_and_transform_data(train_set_filepath)\n",
    "\n",
    "# Preview the first few rows of the training set.\n",
    "print(\"First few rows of the training set:\")\n",
    "train_set.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4688c8e7",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 24,
     "status": "ok",
     "timestamp": 1694843773223,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "4688c8e7",
    "outputId": "195f7d02-80e0-4782-ae54-d21b12390369"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set dimensions: (36808, 7)\n"
     ]
    }
   ],
   "source": [
    "# Display the shape of the training set to understand its size.\n",
    "print(f'Training set dimensions: {train_set.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f291357c",
   "metadata": {
    "id": "f291357c"
   },
   "source": [
    "#### Summary Statistics for Training Set\n",
    "---\n",
    "\n",
    "In this step, we generate **summary statistics** for the training set. This provides insights into central tendencies, dispersion, and other critical aspects of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8f420528",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 23,
     "status": "ok",
     "timestamp": 1694843773223,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "8f420528",
    "outputId": "1f896950-89e4-478f-cad7-77793588109a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary statistics for the training set:\n",
      "              label\n",
      "count  36808.000000\n",
      "mean       0.404070\n",
      "std        0.490718\n",
      "min        0.000000\n",
      "25%        0.000000\n",
      "50%        0.000000\n",
      "75%        1.000000\n",
      "max        1.000000\n"
     ]
    }
   ],
   "source": [
    "# Generate summary statistics for the training set.\n",
    "print(\"Summary statistics for the training set:\")\n",
    "print(train_set.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beb992cf",
   "metadata": {
    "id": "beb992cf"
   },
   "source": [
    "#### Unique Classes in the Training Set\n",
    "---\n",
    "\n",
    "Understanding the unique classes or labels in the dataset is crucial for classification tasks, as it defines what the model will predict. While this procedure may seem straightforward, it's essential to confirm that the labels are of type **0** and **1**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a40169c8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 22,
     "status": "ok",
     "timestamp": 1694843773224,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "a40169c8",
    "outputId": "476ecf27-6c77-4698-9f07-187d90d0a003"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique classes in the training set:\n",
      "[1 0]\n"
     ]
    }
   ],
   "source": [
    "# Display the unique classes in the training set.\n",
    "print(\"Unique classes in the training set:\")\n",
    "print(train_set['label'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2046c05",
   "metadata": {
    "id": "e2046c05"
   },
   "source": [
    "#### Loading and Transforming the Test Data\n",
    "---\n",
    "\n",
    "Similar to the training set, the **test** set also requires loading and preparation. This set will be used to evaluate the model's performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "82a03aa9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 380
    },
    "executionInfo": {
     "elapsed": 22,
     "status": "ok",
     "timestamp": 1694843773225,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "82a03aa9",
    "outputId": "acf55c85-0be7-4a9e-d02c-4fef5174212c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First few rows of the test set:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image_path</th>\n",
       "      <th>description</th>\n",
       "      <th>type</th>\n",
       "      <th>patient</th>\n",
       "      <th>study</th>\n",
       "      <th>opinion</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MURA-v1.1/valid/XR_WRIST/patient11185/study1_p...</td>\n",
       "      <td>WRIST_patient11185_study1_positive</td>\n",
       "      <td>WRIST</td>\n",
       "      <td>patient11185</td>\n",
       "      <td>study1</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MURA-v1.1/valid/XR_WRIST/patient11185/study1_p...</td>\n",
       "      <td>WRIST_patient11185_study1_positive</td>\n",
       "      <td>WRIST</td>\n",
       "      <td>patient11185</td>\n",
       "      <td>study1</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MURA-v1.1/valid/XR_WRIST/patient11185/study1_p...</td>\n",
       "      <td>WRIST_patient11185_study1_positive</td>\n",
       "      <td>WRIST</td>\n",
       "      <td>patient11185</td>\n",
       "      <td>study1</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MURA-v1.1/valid/XR_WRIST/patient11185/study1_p...</td>\n",
       "      <td>WRIST_patient11185_study1_positive</td>\n",
       "      <td>WRIST</td>\n",
       "      <td>patient11185</td>\n",
       "      <td>study1</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MURA-v1.1/valid/XR_WRIST/patient11186/study1_p...</td>\n",
       "      <td>WRIST_patient11186_study1_positive</td>\n",
       "      <td>WRIST</td>\n",
       "      <td>patient11186</td>\n",
       "      <td>study1</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MURA-v1.1/valid/XR_WRIST/patient11186/study1_p...</td>\n",
       "      <td>WRIST_patient11186_study1_positive</td>\n",
       "      <td>WRIST</td>\n",
       "      <td>patient11186</td>\n",
       "      <td>study1</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MURA-v1.1/valid/XR_WRIST/patient11186/study2_p...</td>\n",
       "      <td>WRIST_patient11186_study2_positive</td>\n",
       "      <td>WRIST</td>\n",
       "      <td>patient11186</td>\n",
       "      <td>study2</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MURA-v1.1/valid/XR_WRIST/patient11186/study2_p...</td>\n",
       "      <td>WRIST_patient11186_study2_positive</td>\n",
       "      <td>WRIST</td>\n",
       "      <td>patient11186</td>\n",
       "      <td>study2</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MURA-v1.1/valid/XR_WRIST/patient11186/study2_p...</td>\n",
       "      <td>WRIST_patient11186_study2_positive</td>\n",
       "      <td>WRIST</td>\n",
       "      <td>patient11186</td>\n",
       "      <td>study2</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>MURA-v1.1/valid/XR_WRIST/patient11186/study3_p...</td>\n",
       "      <td>WRIST_patient11186_study3_positive</td>\n",
       "      <td>WRIST</td>\n",
       "      <td>patient11186</td>\n",
       "      <td>study3</td>\n",
       "      <td>positive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          image_path  \\\n",
       "0  MURA-v1.1/valid/XR_WRIST/patient11185/study1_p...   \n",
       "1  MURA-v1.1/valid/XR_WRIST/patient11185/study1_p...   \n",
       "2  MURA-v1.1/valid/XR_WRIST/patient11185/study1_p...   \n",
       "3  MURA-v1.1/valid/XR_WRIST/patient11185/study1_p...   \n",
       "4  MURA-v1.1/valid/XR_WRIST/patient11186/study1_p...   \n",
       "5  MURA-v1.1/valid/XR_WRIST/patient11186/study1_p...   \n",
       "6  MURA-v1.1/valid/XR_WRIST/patient11186/study2_p...   \n",
       "7  MURA-v1.1/valid/XR_WRIST/patient11186/study2_p...   \n",
       "8  MURA-v1.1/valid/XR_WRIST/patient11186/study2_p...   \n",
       "9  MURA-v1.1/valid/XR_WRIST/patient11186/study3_p...   \n",
       "\n",
       "                          description   type       patient   study   opinion  \\\n",
       "0  WRIST_patient11185_study1_positive  WRIST  patient11185  study1  positive   \n",
       "1  WRIST_patient11185_study1_positive  WRIST  patient11185  study1  positive   \n",
       "2  WRIST_patient11185_study1_positive  WRIST  patient11185  study1  positive   \n",
       "3  WRIST_patient11185_study1_positive  WRIST  patient11185  study1  positive   \n",
       "4  WRIST_patient11186_study1_positive  WRIST  patient11186  study1  positive   \n",
       "5  WRIST_patient11186_study1_positive  WRIST  patient11186  study1  positive   \n",
       "6  WRIST_patient11186_study2_positive  WRIST  patient11186  study2  positive   \n",
       "7  WRIST_patient11186_study2_positive  WRIST  patient11186  study2  positive   \n",
       "8  WRIST_patient11186_study2_positive  WRIST  patient11186  study2  positive   \n",
       "9  WRIST_patient11186_study3_positive  WRIST  patient11186  study3  positive   \n",
       "\n",
       "   label  \n",
       "0      1  \n",
       "1      1  \n",
       "2      1  \n",
       "3      1  \n",
       "4      1  \n",
       "5      1  \n",
       "6      1  \n",
       "7      1  \n",
       "8      1  \n",
       "9      1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the test data using our custom function.\n",
    "test_set = load_and_transform_data(test_set_filepath)\n",
    "\n",
    "# Preview the first few rows of the test set.\n",
    "print(\"First few rows of the test set:\")\n",
    "test_set.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1e6f5ffa",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 20,
     "status": "ok",
     "timestamp": 1694843773225,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "1e6f5ffa",
    "outputId": "80804093-4b41-4c5e-8c5a-45d76aeac971"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set dimensions: (3197, 7)\n"
     ]
    }
   ],
   "source": [
    "# Display the shape of the test set to understand its size.\n",
    "print(f'Test set dimensions: {test_set.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e1f9547",
   "metadata": {
    "id": "5e1f9547"
   },
   "source": [
    "#### Summary Statistics for Test Set\n",
    "---\n",
    "\n",
    "In this step, we generate **summary statistics** for the test set, as well. These statistics offer valuable insights into the properties and characteristics of this dataset, contributing to a more thorough analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2b079f03",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1694843773225,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "2b079f03",
    "outputId": "1a91d8d6-3def-4922-eac7-3d83a341390c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary statistics for the test set:\n",
      "             label\n",
      "count  3197.000000\n",
      "mean      0.478574\n",
      "std       0.499619\n",
      "min       0.000000\n",
      "25%       0.000000\n",
      "50%       0.000000\n",
      "75%       1.000000\n",
      "max       1.000000\n"
     ]
    }
   ],
   "source": [
    "# Generate summary statistics for the test set.\n",
    "print(\"Summary statistics for the test set:\")\n",
    "print(test_set.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc839932",
   "metadata": {
    "id": "bc839932"
   },
   "source": [
    "#### Unique Classes in the Test Set\n",
    "---\n",
    "\n",
    "Lastly, we take note of the distinct classes found in the test set. This information will be crucial during the evaluation phase, where we again expect to encounter the classes **0** and **1**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4255c0f6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 18,
     "status": "ok",
     "timestamp": 1694843773226,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "4255c0f6",
    "outputId": "7f949378-62f0-447a-b97c-06a194bf5774"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique classes in the test set:\n",
      "[1 0]\n"
     ]
    }
   ],
   "source": [
    "# Display the unique classes in the test set.\n",
    "print(\"Unique classes in the test set:\")\n",
    "print(test_set['label'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abe4df94",
   "metadata": {
    "id": "abe4df94"
   },
   "source": [
    "### Exploring Distribution of Normal and Abnormal Case Studies Across Anatomical Body Parts in MURA Dataset<a class=\"anchor\" id=\"exploring-distribution-of-normal-and-abnormal-case-studies-across-anatomical-body-parts-in-mura-dataset\"></a>\n",
    "---\n",
    "\n",
    "In preparation for the implementation of Convolutional Neural Networks (CNNs) for the analysis of radiographic images in the MURA dataset, we have created the following **visualizations** to gain a holistic understanding of the distribution of **normal** and **abnormal** case studies across different anatomical body parts.\n",
    "\n",
    "#### Visualizing Distribution with Stacked Bar Plots\n",
    "---\n",
    "Our initial visualization takes the form of a stacked bar plot. This informative plot provides an overview of the total number of normal and abnormal cases for each anatomical body part. It serves as a foundational insight into the distribution of cases across different anatomical regions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2c92a475",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 727
    },
    "executionInfo": {
     "elapsed": 8001,
     "status": "ok",
     "timestamp": 1694843781211,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "2c92a475",
    "outputId": "47bf84b2-b941-4bdd-e8eb-1f17e637df5d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the locale to format numbers with thousand separators.\n",
    "locale.setlocale(locale.LC_ALL, 'en_US.UTF-8')\n",
    "\n",
    "# Set Seaborn style without grid lines.\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Sort the data for better presentation.\n",
    "train_counts = train_set.groupby(['type', 'label']).size().unstack(fill_value=0)\n",
    "\n",
    "# Set the figure size and aspect ratio.\n",
    "fig, ax = plt.subplots(figsize=(12, 8))  # Adjust width and height as needed.\n",
    "ax.set_aspect('auto')  # Adjust 'auto' as needed for the desired aspect ratio.\n",
    "\n",
    "# Create a stacked bar plot.\n",
    "train_counts.plot(kind='bar', stacked=True, color=['#BDE7BD', '#FFB6B3'], width=0.8, ax=ax)\n",
    "\n",
    "# Adding labels and title.\n",
    "ax.set_xlabel('Body Part', fontsize=13)\n",
    "ax.set_ylabel('Number of Case Studies', fontsize=13)\n",
    "ax.set_title('Distribution of Normal and Abnormal Case Studies Across Body Parts', fontsize=14)\n",
    "\n",
    "# Rotate x-axis labels to be parallel to the x-axis.\n",
    "plt.xticks(rotation=0, ha=\"center\", fontsize=10)\n",
    "\n",
    "# Adding data labels with thousand separators.\n",
    "for container in ax.containers:\n",
    "    ax.bar_label(container, padding=-13, labels=[locale.format_string('%d', val, grouping=True) for val in container.datavalues], fontsize=10)\n",
    "\n",
    "# Adding legend.\n",
    "ax.legend(loc='upper left', labels=['Normal', 'Abnormal'], fontsize=10)\n",
    "\n",
    "# Remove grid lines.\n",
    "ax.yaxis.grid(False)\n",
    "ax.xaxis.grid(False)\n",
    "\n",
    "# Set y-axis ticks and labels with 'K' for thousands and exclude 0.\n",
    "yticks = ax.get_yticks()\n",
    "yticklabels = [f'{int(y/1000)}K' if y != 0 else '0' for y in yticks]\n",
    "ax.set_yticks(yticks)\n",
    "ax.set_yticklabels(yticklabels)\n",
    "\n",
    "# Save the plot with a higher DPI (900).\n",
    "plt.savefig('distribution_of_case_studies_per_body_part_train.png', bbox_inches='tight', dpi=900)\n",
    "\n",
    "# Show the plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf3c4a0e",
   "metadata": {
    "id": "bf3c4a0e"
   },
   "source": [
    "#### Visualizing Distribution with Pie Charts\n",
    "---\n",
    "\n",
    "Additionally, we have employed multiple pie charts, each regarding a specific body part, to offer a detailed portrayal of the distribution in terms of percentages. These pie charts present a more granular perspective on the prevalence of normal and abnormal cases within each anatomical category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0fe27698",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 749
    },
    "executionInfo": {
     "elapsed": 3617,
     "status": "ok",
     "timestamp": 1694843784826,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "0fe27698",
    "outputId": "6ff0da34-8c32-4c08-a84f-8e9268092faf"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the locale to format numbers with thousand separators.\n",
    "locale.setlocale(locale.LC_ALL, 'en_US.UTF-8')\n",
    "\n",
    "# Set Seaborn style without grid lines.\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Create a color palette to match the stacked bar plot.\n",
    "colors = ['#BDE7BD', '#FFB6B3']\n",
    "\n",
    "# Set the number of pie charts in each row and column.\n",
    "n_cols = 4  # Adjust the number of columns as needed.\n",
    "n_rows = int(len(train_set['type'].unique()) / n_cols) + 1\n",
    "\n",
    "# Create a figure and axis objects with adjusted size and spacing.\n",
    "fig, axs = plt.subplots(n_rows, n_cols, figsize=(14, 10))  # Adjust the figure size as needed.\n",
    "plt.subplots_adjust(wspace=0.3, hspace=0.5)  # Adjust spacing between pie charts.\n",
    "\n",
    "# Flatten the axis array for easier indexing.\n",
    "axs = axs.flatten()\n",
    "\n",
    "# Loop through each unique body part in the training set.\n",
    "for idx, part in enumerate(train_set['type'].unique()):\n",
    "    # Get a subset of the DataFrame containing only rows of the current body part.\n",
    "    subset = train_set[train_set['type'] == part]\n",
    "\n",
    "    # Plot a pie chart for the subset with matching colors.\n",
    "    pie_data = subset['label'].value_counts()\n",
    "    total_cases = pie_data.sum()\n",
    "    labels = ['Normal', 'Abnormal']\n",
    "    axs[idx].pie(pie_data, labels=labels, autopct='%1.2f%%', colors=colors)\n",
    "\n",
    "    # Set the title to display the body part and total cases.\n",
    "    axs[idx].set_title(f'{part}\\n\\nTotal Cases: {total_cases}\\n', fontsize=12)\n",
    "    axs[idx].set_ylabel('')  # Remove y-label.\n",
    "\n",
    "# Remove any unused subplots.\n",
    "for idx in range(len(train_set['type'].unique()), n_cols * n_rows):\n",
    "    fig.delaxes(axs[idx])\n",
    "\n",
    "# Add an outline border to the final grid in white-gray color.\n",
    "for ax in axs:\n",
    "    for spine in ax.spines.values():\n",
    "        spine.set_color('lightgray')  # You can adjust the color as needed.\n",
    "\n",
    "# Add a common title.\n",
    "fig.suptitle('Distribution of Normal and Abnormal Case Studies Across Body Parts', fontsize=16)\n",
    "\n",
    "# Show all pie charts in one output pane.\n",
    "plt.tight_layout(rect=[0, 0.03, 1, 0.95])  # Adjust the position of the suptitle.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "342a033c",
   "metadata": {
    "id": "342a033c"
   },
   "source": [
    "### General Insights\n",
    "\n",
    "---\n",
    "\n",
    "Upon examination, it becomes evident that the distribution of these case studies is not uniform across all body parts. Some categories exhibit a relatively balanced distribution of abnormal and normal cases, while others show disparities, with a notable prevalence of one over the other.\n",
    "\n",
    "These distribution patterns hold clinical significance as they reflect the real-world prevalence of musculoskeletal conditions in various anatomical regions. Understanding these variations is essential for developing diagnostic algorithms that are both robust and clinically applicable.\n",
    "\n",
    "For example, some body parts, such as the **\"Shoulder\"**, showcase nearly equal numbers of abnormal and normal cases, suggesting a balanced distribution. In contrast, the \"Wrist\" category demonstrates a higher prevalence of normal cases, while the **\"Hand\"** category also leans towards normal cases, though to a lesser extent.\n",
    "\n",
    "\n",
    "#### Challenges in Model Training\n",
    "\n",
    "The observed disparities in case distribution can pose challenges during the training of CNN models. Imbalanced distributions, as seen in the **\"Wrist\"** category, have the potential to introduce biases into the models, which could, in turn, impact their ability to accurately diagnose musculoskeletal conditions. Therefore, these distribution insights inform model development strategies to ensure robust performance across diverse anatomical regions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "445f570b",
   "metadata": {
    "id": "445f570b"
   },
   "source": [
    "### Data Splitting Training and Validation Sets<a class=\"anchor\" id=\"data-splitting-trainning-and-validation-sets\"></a>\n",
    "---\n",
    "\n",
    "The function `train_validation_split` serves a critical role in our machine learning pipeline, particularly given our focus on medical diagnostics. In this context, having a robust method for partitioning the data is not just a matter of performance but also of **clinical reliability**.\n",
    "\n",
    "#### Function Overview\n",
    "---\n",
    "The function takes a `Pandas DataFrame` as its input and returns two separate Data Frames: `train_set` and `valid_set`. These represent the training and validation sets, respectively. The function employs **stratification** to ensure that both output sets are representative of the overall data distribution in terms of both 'label' and 'type' fields.\n",
    "\n",
    "#### Key Steps\n",
    "---\n",
    "\n",
    "1. **Feature and Label Isolation**: The feature set (`x`) is defined by dropping the 'label' column from the original dataset. Simultaneously, the label set (`y`) is isolated.\n",
    "\n",
    "\n",
    "2. **Stratified Random Split**: The function uses the `train_test_split` method from scikit-learn, set to reserve 20% of the dataset for the validation set. The `shuffle=True` parameter ensures the data is randomized before splitting. The `stratify` parameter confirms that the 'label' and 'type' distributions in the training and validation sets match those in the original dataset.\n",
    "\n",
    "\n",
    "3. **Data Concatenation**: After splitting, the function reconstructs the `train_set` and `valid_set` by concatenating the feature and label sets.\n",
    "\n",
    "\n",
    "4. **Dimension Verification**: Finally, the dimensions of the resulting sets are printed for verification and debugging purposes.\n",
    "\n",
    "By following these steps, we maintain the **integrity and representativeness** of our training and validation sets, thereby setting the stage for reliable and reproducible machine learning experiments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d0cb315f",
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1694843784826,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "d0cb315f"
   },
   "outputs": [],
   "source": [
    "# A function that splits the dataset into training and validation sets.\n",
    "def train_validation_split(dataset: pd.DataFrame):\n",
    "\n",
    "    # Define the features (x) and labels (y) for the dataset.\n",
    "    x = dataset.drop(columns='label')  # Drop the label column to isolate the features.\n",
    "    y = dataset.label  # Isolate the label column.\n",
    "\n",
    "    # Perform stratified random splitting of the dataset.\n",
    "    # Stratification ensures that the distribution of 'label' and 'type' is consistent across both sets.\n",
    "    x_train, x_valid, y_train, y_valid = train_test_split(\n",
    "        x, y,\n",
    "        test_size=0.2,  # Reserve 20% of the data for validation.\n",
    "        shuffle=True,  # Shuffle the data before splitting.\n",
    "        stratify=dataset[['label', 'type']],  # Stratify based on the 'label' and 'type' columns.\n",
    "        random_state=1  # Set random state for reproducibility.\n",
    "    )\n",
    "\n",
    "    # Concatenate the features and labels to form the complete training and validation sets.\n",
    "    train_set = pd.concat([x_train, y_train], axis=1)\n",
    "    valid_set = pd.concat([x_valid, y_valid], axis=1)\n",
    "\n",
    "    # Print the shapes of the training and validation sets for immediate feedback.\n",
    "    print(f'Training set dimensions: {train_set.shape}')\n",
    "    print(f'Validation set dimensions: {valid_set.shape}')\n",
    "\n",
    "    return train_set, valid_set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8265668",
   "metadata": {
    "id": "b8265668"
   },
   "source": [
    "### Execution of Data Splitting Function\n",
    "---\n",
    "\n",
    "Upon executing the `train_validation_split` function, the dataset is divided into training and validation sets according to the prescribed methodology. This step is pivotal as it prepares the data for the forthcoming machine learning operations, ensuring that the model will be trained and validated on **distinct subsets** of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0f816d98",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 458,
     "status": "ok",
     "timestamp": 1694843785282,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "0f816d98",
    "outputId": "a9334449-0c70-4267-a53b-2f1433fb8610"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set dimensions: (29446, 7)\n",
      "Validation set dimensions: (7362, 7)\n"
     ]
    }
   ],
   "source": [
    "# Execute the data splitting function to create training and validation sets.\n",
    "train_set, valid_set = train_validation_split(train_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc5354ca",
   "metadata": {
    "id": "cc5354ca"
   },
   "source": [
    "### Analyzing Case Study Distribution for CNN Implementation in the MURA Dataset<a class=\"anchor\" id=\"analyzing-case-study-distribution-for-cnn-implementation-in-the-mura-dataset\"></a>\n",
    "---\n",
    "\n",
    "Our next step is to gain a comprehensive understanding of the distribution of case studies across both the training and validation sets. This knowledge is vital as it informs our approach to model development and evaluation.\n",
    "\n",
    "To visually represent this distribution, we have utilized a **stacked bar chart**. The following chart provides a clear overview of the number of case studies for each anatomical body part, distinguishing between the training  and validation sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9fb82bd6",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 564
    },
    "executionInfo": {
     "elapsed": 2247,
     "status": "ok",
     "timestamp": 1694843787527,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "9fb82bd6",
    "outputId": "55a380ba-75bf-4b3c-d1da-2992d3df0fad"
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Suppress the specific FutureWarning from Seaborn.\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning, module=\"seaborn\")\n",
    "\n",
    "# Set the locale to format numbers with thousand separators.\n",
    "locale.setlocale(locale.LC_ALL, 'en_US.UTF-8')\n",
    "\n",
    "# Set Seaborn style for subtle grid lines.\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Create figure and axes with a specific size.\n",
    "plt.figure(figsize=(10, 6), dpi=100)\n",
    "\n",
    "# Sort the data for better presentation.\n",
    "train_counts = train_set['type'].value_counts().sort_index()\n",
    "valid_counts = valid_set['type'].value_counts().sort_index()\n",
    "\n",
    "# Plot train set.\n",
    "ax = sns.barplot(x=train_counts.index, y=train_counts.values, color='#cdf6ff', label='Train Set')\n",
    "\n",
    "# Plot validation set.\n",
    "sns.barplot(x=valid_counts.index, y=valid_counts.values, color='#50e0ff', ax=ax, label='Validation Set')\n",
    "\n",
    "# Adding labels and title.\n",
    "plt.xlabel('Body Part', fontsize=10)\n",
    "plt.ylabel('Number of Case Studies')\n",
    "plt.title('Distribution of Case Studies Across Body Parts', fontsize=12)\n",
    "\n",
    "# Adjust font size for category names on the x-axis.\n",
    "ax.set_xticklabels(ax.get_xticklabels(), fontsize=8)\n",
    "\n",
    "# Remove y-axis ticks.\n",
    "plt.yticks([])\n",
    "\n",
    "# Adding data labels with thousand separators.\n",
    "for p in ax.patches:\n",
    "    ax.annotate(locale.format_string('%d', p.get_height(), grouping=True),\n",
    "                (p.get_x() + p.get_width() / 2., p.get_height()),\n",
    "                ha='center', va='center',\n",
    "                fontsize=8, color='black',\n",
    "                xytext=(0, 5),\n",
    "                textcoords='offset points')\n",
    "\n",
    "# Adding legend.\n",
    "plt.legend(fontsize=8)\n",
    "\n",
    "# Save the plot.\n",
    "plt.savefig('case_study_distribution.png', bbox_inches='tight', dpi=400)\n",
    "\n",
    "# Show the plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53fc7f5e",
   "metadata": {
    "id": "53fc7f5e"
   },
   "source": [
    "### General Insights\n",
    "---\n",
    "\n",
    "Our analysis of the case study distribution across different anatomical body parts in both the training and validation sets reveals several key insights. Notably, the **\"Wrist\"** and **\"Shoulder\"** categories have the highest number of case studies in both sets, indicating their prevalence in the dataset.\n",
    "\n",
    "Understanding this distribution is essential for model development, as it helps us ensure that our CNN is trained on a diverse range of cases, thus enhancing its ability to generalize and accurately diagnose musculoskeletal conditions across various anatomical regions. Moreover, it informs our strategies for model evaluation, ensuring that our CNN's performance is robust and clinically applicable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "286c4a36",
   "metadata": {
    "id": "286c4a36"
   },
   "source": [
    "### Image Data Generation and Augmentation<a class=\"anchor\" id=\"image-data-generation-and-augmentation\"></a>\n",
    "---\n",
    "Establishing and configuring an `ImageDataGenerator` object is a crucial step in the pipeline, expecially for handling **image data augmentation** and **preprocessing**. These processes not only expand the dataset but also assist the model in generalizing better to unseen data.\n",
    "\n",
    "The following code sets up the `ImageDataGenerator` and generates augmented images from the `train_set`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "10c05517",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 328,
     "status": "ok",
     "timestamp": 1694843787853,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "10c05517",
    "outputId": "a4b4bad9-d3d3-478a-a178-a2b064f7cee6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 29446 validated image filenames.\n"
     ]
    }
   ],
   "source": [
    "# Initialize an ImageDataGenerator object for augmenting and preprocessing the training dataset.\n",
    "augmented_image_data_gen = ImageDataGenerator(\n",
    "    rotation_range=30,                                 # Randomly rotate images up to 30 degrees.\n",
    "    brightness_range=(0.8, 1.2),                       # Randomly adjust the brightness of images.\n",
    "    horizontal_flip=True,                              # Randomly flip images horizontally.\n",
    "    rescale=random.uniform(0.95, 1.3),                 # Randomly rescale the images.\n",
    "    preprocessing_function=preprocess_input            # Apply preprocessing suitable for the third VGG19 model.\n",
    ")\n",
    "\n",
    "# Generate batches of augmented images from the training dataset.\n",
    "augmented_train_data_gen = augmented_image_data_gen.flow_from_dataframe(\n",
    "    train_set,\n",
    "    x_col='image_path',                                # Column containing the image file paths.\n",
    "    y_col='label',                                     # Column containing the image labels.\n",
    "    target_size=(224, 224),                            # Resize images to 224x224 pixels.\n",
    "    class_mode='raw',                                  # Labels are integers, so use 'raw' as the class_mode.\n",
    "    batch_size=64,                                     # Number of samples per gradient update.\n",
    "    shuffle=True,                                      # Shuffle the order of the samples.\n",
    "    seed=seed                                          # Random seed for reproducibility.\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04f91331",
   "metadata": {
    "id": "04f91331"
   },
   "source": [
    "#### Key Takeaways:\n",
    "---\n",
    "\n",
    "1. **Rotation Range**: The `rotation_range` parameter specifies the degree range for random rotations. This introduces a form of rotational invariance to the model.\n",
    "\n",
    "\n",
    "2. **Brightness Range**: Randomly changing the brightness helps the model adapt to varying lighting conditions in the images.\n",
    "\n",
    "\n",
    "3. **Horizontal Flip**: Introduces mirror symmetry to the dataset, which is beneficial as medical conditions can appear on either side of the body.\n",
    "\n",
    "\n",
    "4. **Rescale**: Randomly rescales the image pixel values, making the model robust to slight variations in scale.\n",
    "\n",
    "\n",
    "5. **Preprocessing Function**: The preprocessing function prepares the image for the VGG19 model, normalizing pixel values as required.\n",
    "\n",
    "By executing this piece of code, we generate an `augmented_train_data_gen` object, which serves as the generator for supplying batches of augmented and preprocessed images to the model during the training phase."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18eb2683",
   "metadata": {
    "id": "18eb2683"
   },
   "source": [
    "### Image Data Preprocessing for Validation Set\n",
    "---\n",
    "\n",
    "In the following code block, we prepare the validation images for model evaluation. Unlike the training images, validation images are not augmented, but they are preprocessed to match the input requirements of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7485cb3e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1694843787853,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "7485cb3e",
    "outputId": "d8f92e2b-741e-4bc0-8950-ade057b8c772"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 7362 validated image filenames.\n"
     ]
    }
   ],
   "source": [
    "# Initialize an ImageDataGenerator object for preprocessing the validation dataset.\n",
    "validation_image_data_gen = ImageDataGenerator(\n",
    "    preprocessing_function=preprocess_input  # Apply preprocessing suitable for the third model (VGG).\n",
    ")\n",
    "\n",
    "# Generate batches of preprocessed images from the validation dataset.\n",
    "preprocessed_valid_data_gen = validation_image_data_gen.flow_from_dataframe(\n",
    "    valid_set,\n",
    "    x_col='image_path',                        # Column containing the image file paths.\n",
    "    y_col='label',                             # Column containing the image labels.\n",
    "    target_size=(224, 224),                    # Resize images to 224x224 pixels.\n",
    "    class_mode='raw',                          # Labels are integers, so use 'raw' as the class_mode.\n",
    "    batch_size=64,                             # Number of samples per gradient update.\n",
    "    shuffle=True,                              # Shuffle the order of the samples.\n",
    "    seed=seed                                  # Random seed for reproducibility.\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5148fbc",
   "metadata": {
    "id": "e5148fbc"
   },
   "source": [
    "#### Key Takeaways:\n",
    "---\n",
    "\n",
    "1. **Preprocessing Function**: The `preprocessing_function` normalizes the pixel values of the images, making them suitable for input into the VGG19 model.\n",
    "\n",
    "\n",
    "2. **Target Size**: All images are resized to 224x224 pixels to conform to the input size requirements of the VGG19 model.\n",
    "\n",
    "\n",
    "3. **Class Mode**: We set the `class_mode` to 'raw' because the labels in our dataset are integers.\n",
    "\n",
    "\n",
    "4. **Batch Size**: The `batch_size` parameter controls the number of images processed in each batch during model evaluation.\n",
    "\n",
    "\n",
    "5. **Shuffling and Seed**: We shuffle the images to ensure randomness but also set a random seed for reproducibility.\n",
    "\n",
    "By executing this piece of code, we create a `preprocessed_valid_data_gen` object that functions as the generator for feeding batches of preprocessed images into the model during the validation phase."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df74eebf",
   "metadata": {
    "id": "df74eebf"
   },
   "source": [
    "### Image Data Preprocessing for Test Set\n",
    "---\n",
    "\n",
    "In the subsequent code block, we prepare the test images for model evaluation. Similar to the validation images, the test images are not augmented but are preprocessed to fit the model's input requirements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1884dd61",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1694843787854,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "1884dd61",
    "outputId": "48eb7350-817b-4f2e-9677-fb5d3f4ad5d6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 3197 validated image filenames.\n"
     ]
    }
   ],
   "source": [
    "# Initialize an ImageDataGenerator object for preprocessing the test dataset.\n",
    "testImagePreprocessor = ImageDataGenerator(\n",
    "    preprocessing_function=preprocess_input  # Apply preprocessing suitable for the VGG19 model.\n",
    ")\n",
    "\n",
    "# Generate batches of preprocessed images from the test dataset.\n",
    "preprocessedTestDataGen = testImagePreprocessor.flow_from_dataframe(\n",
    "    test_set,\n",
    "    x_col='image_path',                      # Column containing the image file paths.\n",
    "    y_col='label',                           # Column containing the image labels.\n",
    "    target_size=(224, 224),                  # Resize images to 224x224 pixels.\n",
    "    class_mode='raw',                        # Labels are integers, so use 'raw' as the class_mode.\n",
    "    batch_size=64,                           # Number of samples per gradient update.\n",
    "    shuffle=False,                           # Do not shuffle the order of the samples.\n",
    "    seed=seed                                # Random seed for reproducibility.\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "82KMsLu9eTgT",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1694843788261,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "82KMsLu9eTgT",
    "outputId": "1a19fe5a-c9c9-49c3-c4f7-66487ab710e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                          image_path  \\\n",
      "0  MURA-v1.1/valid/XR_WRIST/patient11185/study1_p...   \n",
      "1  MURA-v1.1/valid/XR_WRIST/patient11185/study1_p...   \n",
      "2  MURA-v1.1/valid/XR_WRIST/patient11185/study1_p...   \n",
      "3  MURA-v1.1/valid/XR_WRIST/patient11185/study1_p...   \n",
      "4  MURA-v1.1/valid/XR_WRIST/patient11186/study1_p...   \n",
      "\n",
      "                          description   type       patient   study   opinion  \\\n",
      "0  WRIST_patient11185_study1_positive  WRIST  patient11185  study1  positive   \n",
      "1  WRIST_patient11185_study1_positive  WRIST  patient11185  study1  positive   \n",
      "2  WRIST_patient11185_study1_positive  WRIST  patient11185  study1  positive   \n",
      "3  WRIST_patient11185_study1_positive  WRIST  patient11185  study1  positive   \n",
      "4  WRIST_patient11186_study1_positive  WRIST  patient11186  study1  positive   \n",
      "\n",
      "   label  \n",
      "0      1  \n",
      "1      1  \n",
      "2      1  \n",
      "3      1  \n",
      "4      1  \n"
     ]
    }
   ],
   "source": [
    "# Print the first few rows of the DataFrame.\n",
    "print(test_set.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b2e69ac",
   "metadata": {
    "id": "5b2e69ac"
   },
   "source": [
    "#### Key Takeaways:\n",
    "---\n",
    "\n",
    "1. **Preprocessing Function**: The `preprocessing_function` parameter normalizes the pixel values of the images, making them suitable for input into the VGG19 model.\n",
    "\n",
    "\n",
    "2. **Target Size**: Similar to the training and validation sets, all images are resized to 224x224 pixels to conform to the input size requirements of the VGG19 model.\n",
    "\n",
    "\n",
    "3. **Class Mode**: As with the training and validation sets, we set the `class_mode` to 'raw' since the labels in our dataset are integers.\n",
    "\n",
    "\n",
    "4. **Batch Size**: The `batch_size` parameter determines the number of images processed in each batch during model evaluation.\n",
    "\n",
    "\n",
    "5. **Shuffling and Seed**: Unlike the training and validation sets, we set `shuffle` to False for the test set to maintain the original order of the images. We also set a random seed for reproducibility.\n",
    "\n",
    "By executing this code block, we create a `preprocessedTestDataGen` object that functions as the generator for feeding batches of preprocessed images into the model during the testing phase."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20c40418",
   "metadata": {
    "id": "20c40418"
   },
   "source": [
    "## Building a VGG-19 - Based Binary Classification Model<a class=\"anchor\" id=\"building-a-vgg19-based-binary-classification-model\"></a>\n",
    "---\n",
    "\n",
    "The **VGG19 architecture** is a pivotal milestone in the realm of **deep learning**, particularly in the context of **image classification** tasks. Developed as an extension over the [**VGG16 model**](https://www.geeksforgeeks.org/vgg-16-cnn-model/), VGG19 comprises a total of **19 layers**. These layers include a meticulously structured series of **convolutional layers** for **feature extraction**, followed by **max-pooling layers** for **down-sampling**, and **fully connected layers** for the final **classification task**.\n",
    "\n",
    "\n",
    "A unique **design choice** in VGG19 is the **uniform use of 3x3 filters** across all its convolutional layers. This aspect not only reduces the **total number of parameters** but also ensures **high performance**. The architecture has been rigorously validated to yield **state-of-the-art results** on various **benchmark datasets** for image classification, most notably the **ImageNet dataset**.\n",
    "\n",
    "\n",
    "Beyond its primary application in image classification, VGG19 has been adapted for a **wide array of computer vision tasks**. It serves as a **pre-trained model** in scenarios involving **transfer learning** and has been customized for specialized tasks like **object detection** and **segmentation**.\n",
    "\n",
    "\n",
    "Aligned with the **robust capabilities** offered by the VGG19 architecture, the objective of this notebook is to construct a **specialized VGG-19-based model for binary classification tasks**. We employ the **VGG-19 pre-trained model** as the backbone for **feature extraction** and augment it with **additional layers** tailored for **binary classification**. In subsequent sections, we will outline the **key components** of our custom model and explore opportunities for **further optimization**. In the following section we will deep more into our VGG19 model.\n",
    "\n",
    "\n",
    "#### **Key Components of the Model**:\n",
    "\n",
    "1. **VGG-19 Base Model**: The architecture incorporates the VGG-19 pre-trained model with the top fully connected layers removed, serving as a robust feature extractor.\n",
    "\n",
    "\n",
    "2. **Pooling Layer**: Users can specify the type of pooling (e.g., 'max' or 'average') through the `pooling` argument.\n",
    "\n",
    "\n",
    "3. **Dense Layer**: A fully connected dense layer follows, with customizable parameters like the number of units (default is 512) and the activation function (default is 'ReLU').\n",
    "\n",
    "\n",
    "4. **Batch Normalization**: To normalize the activations, a batch normalization layer is added post the dense layer.\n",
    "\n",
    "\n",
    "5. **Dropout Layer**: To mitigate the risk of overfitting, a dropout layer with a customizable rate (default is 20%) is included.\n",
    "\n",
    "\n",
    "6. **Output Layer**: The final layer is designed for binary classification and employs a sigmoid activation function.\n",
    "\n",
    "\n",
    "7. **Flexibility**: The model's architecture allows for high customization through function arguments, catering to various use-cases and requirements.\n",
    "\n",
    "#### Enhancements and Customizations\n",
    "\n",
    "1. **Model Compilation**: This function defines but doesn't compile the model. We can compile the model separately, specifying the optimizer, loss function, and evaluation metrics.\n",
    "\n",
    "\n",
    "2. **Detailed Documentation**: The function includes a comprehensive docstring and comments for better clarity and understanding.\n",
    "\n",
    "\n",
    "3. **Regularization**: The dense layer includes L2 regularization to prevent overfitting, with customization allowed.\n",
    "\n",
    "\n",
    "4. **Return Values**: The function returns both the base VGG-19 model and the final binary classification model. Users can choose which to employ based on their specific use-case.\n",
    "\n",
    "\n",
    "5. **Parameter Validation**: Future versions may include validation steps for function arguments to ensure they meet specific criteria (e.g., valid activation functions, dropout rate between 0 and 1).\n",
    "\n",
    "The code for model building follows below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3a77f70a",
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1694843788262,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "3a77f70a"
   },
   "outputs": [],
   "source": [
    "def build_model(input_shape=(224, 224, 3),\n",
    "                pooling='max',\n",
    "                hidden_units=512,\n",
    "                hidden_activation='relu',\n",
    "                hidden_dropout_prob=0.2,\n",
    "                output_activation='sigmoid',\n",
    "                l2_reg=0.01):\n",
    "\n",
    "    # Builds and returns a VGG-19-based binary classification model.\n",
    "    base_model = VGG19(include_top=False,\n",
    "                       weights='imagenet',\n",
    "                       input_shape=input_shape,\n",
    "                       pooling=pooling)  # Initialize VGG-19 as the base model.\n",
    "\n",
    "    x = base_model.output  # Get the output of the base model.\n",
    "\n",
    "    x = Dense(units=hidden_units,\n",
    "              kernel_initializer='glorot_uniform',\n",
    "              activation=hidden_activation,\n",
    "              kernel_regularizer=l2(l2_reg),\n",
    "              name='Dense-1')(x)  # Add a Dense layer with L2 regularization.\n",
    "\n",
    "    x = BatchNormalization(name='BatchNormalization-1')(x)  # Add a Batch Normalization layer.\n",
    "\n",
    "    x = Dropout(rate=hidden_dropout_prob, name='Dropout-1')(x)  # Add a Dropout layer to prevent overfitting.\n",
    "\n",
    "    output = Dense(units=1,\n",
    "                   kernel_initializer='glorot_uniform',\n",
    "                   activation=output_activation,\n",
    "                   name='Output')(x)  # Add the Output layer for binary classification.\n",
    "\n",
    "    model = Model(inputs=base_model.input, outputs=output)  # Compile the model architecture.\n",
    "\n",
    "    return base_model, model  # Return the base and final models."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43594574",
   "metadata": {
    "id": "43594574"
   },
   "source": [
    "### Incorporating Callbacks to Enhance Model Training\n",
    "---\n",
    "\n",
    "In this section, we discuss the utilization of **Keras Callbacks** to improve the efficiency and effectiveness of our model training process. These callbacks monitor specific metrics and apply changes to the model or training process based on the observed values. Here are the two primary callbacks used:\n",
    "\n",
    "1. **Early Stopping**: This callback monitors the `val_loss` during the training process. If the validation loss does not improve for 10 consecutive epochs, the training will be terminated. This mechanism aids in avoiding overfitting and ensures that the model generalizes well to new data. Furthermore, the best model weights are restored, meaning we always have the most optimal model up to that point.\n",
    "\n",
    "\n",
    "2. **Reduce Learning Rate on Plateau**: This callback also monitors the `val_loss`. If no improvement is observed for 5 consecutive epochs, the learning rate is reduced by a factor of 0.1. This adaptive learning rate can help the model to overcome local minima or speed up convergence. The learning rate will never go below $1 \\times 10^{-10}$, providing a lower bound.\n",
    "\n",
    "\n",
    "3. **ModelCheckpoint**: This callback is designed for optimal model weight storage and efficient training. It saves the model weights to a specified **filepath**, allowing for easy model retrieval later. The **monitor='val_loss'** parameter ensures that weights are updated only when there's an improvement in the validation loss. By setting **save_weights_only=True**, the callback saves only the model's weights, optimizing storage usage. The **mode='min'** parameter aims to minimize the monitored metric (`val_loss`), ensuring that the model weights are only updated when a lower validation loss is achieved. Finally, **save_best_only=True** ensures that only the best weights, as determined by the monitored metric, are saved. This guarantees that the most effective version of the model is always available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "91806fb3",
   "metadata": {
    "executionInfo": {
     "elapsed": 4295,
     "status": "ok",
     "timestamp": 1694843792555,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "91806fb3"
   },
   "outputs": [],
   "source": [
    "# Generate a timestamp for the current time.\n",
    "current_time = datetime.now().strftime(\"%Y-%m-%d_%H-%M-%S\")\n",
    "\n",
    "# Create a checkpoint path specific to the VGG-19 model.\n",
    "checkpoint_filepath = f'checkpoints/muramed_vgg19_{current_time}/weights.hdf5'\n",
    "\n",
    "# Define EarlyStopping Callback.\n",
    "early_stopping = EarlyStopping(\n",
    "    monitor='val_loss',             # Monitors the validation loss for improvements.\n",
    "    patience=10,                    # Number of epochs with no improvement to wait before stopping.\n",
    "    mode='min',                     # Minimization mode for loss.\n",
    "    restore_best_weights=True,      # Restores model weights from the epoch with the best value of monitored metric.\n",
    "    verbose=1                       # Level of verbosity.\n",
    ")\n",
    "\n",
    "# Define ReduceLROnPlateau Callback.\n",
    "reduce_lr = ReduceLROnPlateau(\n",
    "    monitor='val_loss',             # Monitors the validation loss for improvements.\n",
    "    patience=5,                     # Number of epochs with no improvement after which learning rate will be reduced.\n",
    "    mode='min',                     # Minimization mode for loss.\n",
    "    factor=0.1,                     # Factor by which the learning rate will be reduced.\n",
    "    min_lr=1e-10,                   # Lower bound on the learning rate.\n",
    "    verbose=1                       # Level of verbosity.\n",
    ")\n",
    "\n",
    "# Define ModelCheckpoint Callback.\n",
    "model_checkpoint_callback = ModelCheckpoint(\n",
    "    filepath=checkpoint_filepath,   # Save model weights to a file path that includes the model's name and current timestamp.\n",
    "    save_weights_only=True,         # Only save the model's weights.\n",
    "    monitor='val_loss',             # Monitors the validation loss for improvements.\n",
    "    mode='min',                     # Minimization mode for loss.\n",
    "    save_best_only=True             # Only save the model's weights if they are the best so far.\n",
    ")\n",
    "\n",
    "# Aggregate Callbacks for the custom VGG-19 model.\n",
    "custom_callbacks = [early_stopping, reduce_lr, model_checkpoint_callback]\n",
    "\n",
    "# Custom metrics for VGG19 model.\n",
    "custom_metrics = [Precision(name='precision'),\n",
    "                  Recall(name='recall'),\n",
    "                  BinaryAccuracy(name='bin_accuracy'),\n",
    "                  AUC(name='auc_roc'),\n",
    "                  CohenKappa(name='cohen_kappa', num_classes=2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b946c1f8",
   "metadata": {
    "id": "b946c1f8"
   },
   "source": [
    "By incorporating these callbacks, we add an adaptive layer to our training regimen, allowing the model to adjust itself for optimized performance. Both of these callbacks are aggregated into a list named `CALLBACKS`, which will be used during model training.\n",
    "\n",
    "Also we introduce custom metrics that are specifically tailored for evaluating the performance of our VGG-19 model. Each of these metrics serves a distinct purpose and provides unique insights into the model's effectiveness:\n",
    "\n",
    "1. **Precision (name='precision')**: This metric measures the ratio of true positive predictions to the sum of true positives and false positives. It evaluates the model's ability to correctly identify only the relevant instances.\n",
    "\n",
    "\n",
    "2. **Recall (name='recall')**: Also known as sensitivity, this metric calculates the ratio of true positive predictions to the sum of true positives and false negatives. It assesses the model's capability to identify all relevant instances.\n",
    "\n",
    "\n",
    "3. **Binary Accuracy (name='bin_accuracy')**: This metric computes the mean accuracy rate across all predictions for binary classification problems. It provides a straightforward way to evaluate the model's general performance.\n",
    "\n",
    "\n",
    "4. **AUC-ROC (name='auc_roc')**: The Area Under the Receiver Operating Characteristic curve is a performance metric for binary classification problems. It measures the ability of the model to distinguish between classes at various threshold settings.\n",
    "\n",
    "\n",
    "5. **Cohen's Kappa (name='cohen_kappa', num_classes=2)**: This statistic measures inter-rater agreement for qualitative items. It is generally thought to be a more robust measure than simple percent agreement calculation, as it takes into account the possibility of the agreement occurring by chance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19906cdc",
   "metadata": {
    "id": "19906cdc"
   },
   "source": [
    "### VGG-19-Based Model Training Procedure<a class=\"anchor\" id=\"vgg-19-based-model-training-procedure\"></a>\n",
    "---\n",
    "\n",
    "In this section, we move on to the **intricacies and key components** of our VGG-19-based binary classification model's training process, which is orchestrated through the function `train_VGG19_binary_classification_model`. This function serves as the **central hub** for model training, laying the foundation for the machine learning pipeline. Let's delve into its major elements:\n",
    "\n",
    "1. **Model Initialization**: The function commences by invoking the `build_model` function to construct the **VGG-19-based architecture**. This foundational step ensures the model is compiled with the appropriate parameters, such as learning rate and loss function.\n",
    "\n",
    "\n",
    "2. **Layer Freezing and Pre-training**: In the initial phase, the function sets the trainable parameters of the base VGG-19 model to `False`. This allows for **focused training** on the additional dense, dropout, and batch normalization layers, optimizing them for the specific task at hand.\n",
    "\n",
    "\n",
    "3. **Batch and Epoch Configuration**: The function accommodates **flexible configurations** for both batch size and the number of epochs, facilitating fine-tuning for specific performance requirements.\n",
    "\n",
    "\n",
    "4. **Callbacks**: The function can implement a series of Keras callbacks, such as **Early Stopping** and **Learning Rate Reduction**, to enhance the training process and prevent overfitting.\n",
    "\n",
    "\n",
    "5. **Custom Metrics**: The function is designed to monitor **multiple metrics**, offering a comprehensive view of the model's performance during the training phase.\n",
    "\n",
    "\n",
    "6. **Verbose Output**: To provide real-time insights, the function offers **verbose logging** during training. This includes details about the loss and metric values at the end of each epoch.\n",
    "\n",
    "\n",
    "7. **Final Training Phase**: After the initial training of the newly added layers, the function unfreezes the base VGG-19 layers and performs **full network training**. This second phase is crucial for fine-tuning the entire model for the specific binary classification task.\n",
    "\n",
    "\n",
    "8. **Return Values**: At the conclusion of the training process, the function returns the **trained model** along with the **history objects** (`hs_upper_layers` and `hs_full_network`) for both training phases. These can be leveraged for further evaluation and analysis.\n",
    "\n",
    "\n",
    "By encapsulating all these functionalities, `train_VGG19_binary_classification_model` serves as a **comprehensive solution** for training our VGG-19-based binary classification model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9e927056",
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1694843792555,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "9e927056"
   },
   "outputs": [],
   "source": [
    "def train_VGG19_binary_classification_model(train_data,                        # Contains both x_train and y_train.\n",
    "                                            valid_data,                        # Contains both x_valid and y_valid.\n",
    "                                            input_shape=(224, 224, 3),         # Input shape for VGG-19-based model.\n",
    "                                            pooling='max',                     # Pooling type applied to VGG-19 base model.\n",
    "                                            hidden_units=512,                  # Number of neurons in the hidden layer.\n",
    "                                            hidden_activation='relu',          # Activation function for hidden layer.\n",
    "                                            hidden_dropout_rate=0.2,           # Dropout rate for hidden layer.\n",
    "                                            output_activation='sigmoid',       # Output layer activation function for binary classification.\n",
    "                                            upper_layers_learning_rate=1e-4,   # Learning rate for training upper layers.\n",
    "                                            full_network_learning_rate=1e-5,   # Learning rate for training the full network.\n",
    "                                            metrics=custom_metrics,            # Evaluation metrics.\n",
    "                                            batch_size=8,                      # Batch size for training.\n",
    "                                            upper_layers_epochs=5,             # Number of epochs for training upper layers.\n",
    "                                            full_network_epochs=15,            # Number of epochs for training the full network.\n",
    "                                            verbose=1,                         # Verbosity level.\n",
    "                                            callbacks=custom_callbacks         # List of callbacks.\n",
    "                                            ):\n",
    "\n",
    "    # Build the custom VGG-19-based binary classification model.\n",
    "    base_model, model = build_model(input_shape, pooling, hidden_units, hidden_activation, hidden_dropout_rate, output_activation)\n",
    "\n",
    "    # Freeze the VGG-19 base model layers for initial training of upper layers.\n",
    "    for layer in base_model.layers:\n",
    "        layer.trainable = False\n",
    "\n",
    "    # Compile the model with upper layers learning rate.\n",
    "    model.compile(optimizer=Adam(learning_rate=upper_layers_learning_rate), loss='binary_crossentropy', metrics=metrics)\n",
    "\n",
    "    # Train the upper layers of the model.\n",
    "    hs_upper_layers = model.fit(x=train_data,\n",
    "                                batch_size=batch_size,\n",
    "                                epochs=upper_layers_epochs,\n",
    "                                verbose=verbose,\n",
    "                                callbacks=callbacks,\n",
    "                                validation_data=valid_data\n",
    "                                )\n",
    "\n",
    "    # Indicate completion of upper layers training.\n",
    "    print()\n",
    "    print('----------------------------------------------------------------')\n",
    "    print('Finished training upper layers of the custom VGG-19-based model.')\n",
    "    print('----------------------------------------------------------------')\n",
    "    print()\n",
    "\n",
    "    # Unfreeze the VGG-19 base model layers for full network training.\n",
    "    for layer in base_model.layers:\n",
    "        layer.trainable = True\n",
    "\n",
    "    # Compile the model with full network learning rate.\n",
    "    model.compile(optimizer=Adam(learning_rate=full_network_learning_rate), loss='binary_crossentropy', metrics=metrics)\n",
    "\n",
    "    # Train the full network.\n",
    "    hs_full_network = model.fit(x=train_data,\n",
    "                                batch_size=batch_size,\n",
    "                                epochs=full_network_epochs,\n",
    "                                verbose=verbose,\n",
    "                                callbacks=callbacks,\n",
    "                                validation_data=valid_data\n",
    "                                )\n",
    "\n",
    "    # Indicate completion of full network training.\n",
    "    print()\n",
    "    print('-----------------------------------------------------------------------------')\n",
    "    print('Finished training the full custom VGG-19-based binary classification model.')\n",
    "    print('-----------------------------------------------------------------------------')\n",
    "    print()\n",
    "\n",
    "    return model, hs_upper_layers, hs_full_network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5b82beb",
   "metadata": {
    "id": "b5b82beb"
   },
   "source": [
    "### Training Phases in VGG-19 Based Model: Upper Layers and Full Network<a class=\"anchor\" id=\"training-phases-in-vgg-19-based-model\"></a>\n",
    "---\n",
    "\n",
    "In this section, we articulate the **key steps** for executing our VGG-19-based binary classification model's training regimen. The code block embodies several essential elements:\n",
    "\n",
    "1. **Start Time**: As a preliminary step, we capture the system's current time. This metric serves as a **baseline for assessing the model's training duration**.\n",
    "\n",
    "\n",
    "2. **Model Training**: At the heart of this section is the invocation of our `train_VGG19_binary_classification_model` function. This is where the **VGG-19-based model undergoes its comprehensive training regimen**, incorporating both the initial phase focusing on the upper layers and the final phase for fine-tuning the entire network. We pass in our augmented training data, preprocessed validation data, and other pertinent parameters such as epochs and callbacks.\n",
    "\n",
    "\n",
    "3. **Callbacks**: Our training process leverages a suite of Keras callbacks, including **Early Stopping and Learning Rate Reduction**. These mechanisms add an extra layer of optimization, enhancing the model's training efficiency while keeping also the best model (the one with the minimum validation loss).\n",
    "\n",
    "\n",
    "4. **End Time**: Upon the completion of the model's training, we again capture the system's current time. This allows us to **compute the total duration of the training process**.\n",
    "\n",
    "\n",
    "5. **Elapsed Time**: In the concluding step, we calculate and display the **total time elapsed during the model's training**. This provides invaluable insights into the computational resources required for the model's training.\n",
    "\n",
    "\n",
    "Furthermore, the training of our VGG-19-based binary classification model is divided into two main phases: training of the **Upper Layers** and training of the **Full Network**. Each phase serves a distinct purpose and contributes to the model's overall performance. Below, we delve into the rationale behind this two-phase training approach.\n",
    "\n",
    "#### **1. Upper Layers Training Phase**\n",
    "\n",
    "In the first phase, the focus is on **training only the newly added layers**, which are the dense, dropout, and batch normalization layers stacked on top of the pre-trained VGG-19 base model. During this phase:\n",
    "\n",
    "- **Base Model Freezing**: The trainable parameters in the base VGG-19 model are set to `False`. This ensures that the weights of the pre-trained layers remain constant.\n",
    "  \n",
    "  \n",
    "- **Targeted Learning**: This phase allows for more rapid and targeted learning for the task-specific layers. Since these new layers are initialized with random weights, quick convergence is essential for them to start extracting relevant features.\n",
    "\n",
    "\n",
    "- **High Learning Rate**: Generally, a higher learning rate is applied during this phase to speed up the convergence of the new layers.\n",
    "\n",
    "#### **2. Full Network Training Phase**\n",
    "\n",
    "After the upper layers have been pre-trained, the model transitions into the second phase where the **entire network, including the base VGG-19 layers, is fine-tuned**. During this phase:\n",
    "\n",
    "- **Unfreezing Base Model**: The trainable parameters in the base VGG-19 model are set to `True`, enabling backpropagation through the entire network.\n",
    "\n",
    "\n",
    "- **Fine-Tuning**: With a pre-trained base and well-initialized upper layers, the model can be fine-tuned to better adapt to the specific task at hand.\n",
    "\n",
    "\n",
    "- **Low Learning Rate**: A lower learning rate is typically used to make minor adjustments to the weights, ensuring that the model doesn't unlearn the useful features captured in the first phase.\n",
    "\n",
    "#### **Benefits of Two-Phase Training**\n",
    "\n",
    "- **Efficiency**: The two-phase approach often speeds up the training process and requires fewer computational resources compared to training the entire network from scratch.\n",
    "\n",
    "\n",
    "- **Optimization**: This approach aids in better optimization of the model as it allows for targeted learning in each phase, thereby improving the model's performance on the specific task.\n",
    "\n",
    "\n",
    "- **Avoiding Overfitting**: By using a pre-trained model and fine-tuning it, the likelihood of overfitting can be reduced, especially when the dataset is small or not diverse enough.\n",
    "\n",
    "By employing this two-phase training strategy, we aim to harness the **best of both worlds**: the feature extraction capabilities of the pre-trained VGG-19 model and the task-specific adaptability of the newly added layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "60801950",
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     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 13420671,
     "status": "ok",
     "timestamp": 1694857213224,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "60801950",
    "outputId": "0add4f78-bc6e-4040-a774-64f69ff1f5f8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "80134624/80134624 [==============================] - 3s 0us/step\n",
      "Epoch 1/5\n",
      "461/461 [==============================] - 704s 1s/step - loss: 4.6213 - precision: 0.5438 - recall: 0.5487 - bin_accuracy: 0.6316 - auc_roc: 0.6626 - cohen_kappa: 0.2362 - val_loss: 3.6804 - val_precision: 0.7304 - val_recall: 0.3224 - val_bin_accuracy: 0.6781 - val_auc_roc: 0.7282 - val_cohen_kappa: 0.2655 - lr: 1.0000e-04\n",
      "Epoch 2/5\n",
      "461/461 [==============================] - 723s 2s/step - loss: 3.1356 - precision: 0.6163 - recall: 0.5327 - bin_accuracy: 0.6772 - auc_roc: 0.7147 - cohen_kappa: 0.3147 - val_loss: 2.6666 - val_precision: 0.6755 - val_recall: 0.5143 - val_bin_accuracy: 0.7039 - val_auc_roc: 0.7495 - val_cohen_kappa: 0.3606 - lr: 1.0000e-04\n",
      "Epoch 3/5\n",
      "461/461 [==============================] - 682s 1s/step - loss: 2.3898 - precision: 0.6297 - recall: 0.5388 - bin_accuracy: 0.6856 - auc_roc: 0.7324 - cohen_kappa: 0.3317 - val_loss: 2.1246 - val_precision: 0.6355 - val_recall: 0.6094 - val_bin_accuracy: 0.7009 - val_auc_roc: 0.7484 - val_cohen_kappa: 0.3748 - lr: 1.0000e-04\n",
      "Epoch 4/5\n",
      "461/461 [==============================] - 623s 1s/step - loss: 1.9326 - precision: 0.6467 - recall: 0.5605 - bin_accuracy: 0.6987 - auc_roc: 0.7432 - cohen_kappa: 0.3607 - val_loss: 1.7899 - val_precision: 0.7840 - val_recall: 0.3452 - val_bin_accuracy: 0.6970 - val_auc_roc: 0.7524 - val_cohen_kappa: 0.3085 - lr: 1.0000e-04\n",
      "Epoch 5/5\n",
      "461/461 [==============================] - 638s 1s/step - loss: 1.6244 - precision: 0.6526 - recall: 0.5544 - bin_accuracy: 0.7007 - auc_roc: 0.7457 - cohen_kappa: 0.3631 - val_loss: 1.5053 - val_precision: 0.6104 - val_recall: 0.6820 - val_bin_accuracy: 0.6956 - val_auc_roc: 0.7633 - val_cohen_kappa: 0.3797 - lr: 1.0000e-04\n",
      "\n",
      "----------------------------------------------------------------\n",
      "Finished training upper layers of the custom VGG-19-based model.\n",
      "----------------------------------------------------------------\n",
      "\n",
      "Epoch 1/15\n",
      "461/461 [==============================] - 685s 1s/step - loss: 1.4411 - precision: 0.6801 - recall: 0.6100 - bin_accuracy: 0.7264 - auc_roc: 0.7804 - cohen_kappa: 0.4224 - val_loss: 1.4132 - val_precision: 0.8575 - val_recall: 0.4612 - val_bin_accuracy: 0.7513 - val_auc_roc: 0.8185 - val_cohen_kappa: 0.4421 - lr: 1.0000e-05\n",
      "Epoch 2/15\n",
      "461/461 [==============================] - 720s 2s/step - loss: 1.3567 - precision: 0.7561 - recall: 0.6195 - bin_accuracy: 0.7655 - auc_roc: 0.8192 - cohen_kappa: 0.4985 - val_loss: 1.3279 - val_precision: 0.7435 - val_recall: 0.6578 - val_bin_accuracy: 0.7700 - val_auc_roc: 0.8301 - val_cohen_kappa: 0.5135 - lr: 1.0000e-05\n",
      "Epoch 3/15\n",
      "461/461 [==============================] - 662s 1s/step - loss: 1.2963 - precision: 0.7704 - recall: 0.6483 - bin_accuracy: 0.7798 - auc_roc: 0.8376 - cohen_kappa: 0.5309 - val_loss: 1.2892 - val_precision: 0.8365 - val_recall: 0.5694 - val_bin_accuracy: 0.7810 - val_auc_roc: 0.8385 - val_cohen_kappa: 0.5207 - lr: 1.0000e-05\n",
      "Epoch 4/15\n",
      "461/461 [==============================] - 665s 1s/step - loss: 1.2448 - precision: 0.7874 - recall: 0.6701 - bin_accuracy: 0.7936 - auc_roc: 0.8517 - cohen_kappa: 0.5609 - val_loss: 1.2427 - val_precision: 0.8666 - val_recall: 0.5832 - val_bin_accuracy: 0.7953 - val_auc_roc: 0.8484 - val_cohen_kappa: 0.5514 - lr: 1.0000e-05\n",
      "Epoch 5/15\n",
      "461/461 [==============================] - 666s 1s/step - loss: 1.2030 - precision: 0.7993 - recall: 0.6777 - bin_accuracy: 0.8010 - auc_roc: 0.8593 - cohen_kappa: 0.5765 - val_loss: 1.2215 - val_precision: 0.8705 - val_recall: 0.5671 - val_bin_accuracy: 0.7910 - val_auc_roc: 0.8526 - val_cohen_kappa: 0.5401 - lr: 1.0000e-05\n",
      "Epoch 6/15\n",
      "461/461 [==============================] - 668s 1s/step - loss: 1.1673 - precision: 0.8028 - recall: 0.6852 - bin_accuracy: 0.8048 - auc_roc: 0.8639 - cohen_kappa: 0.5848 - val_loss: 1.1811 - val_precision: 0.8501 - val_recall: 0.6061 - val_bin_accuracy: 0.7976 - val_auc_roc: 0.8510 - val_cohen_kappa: 0.5594 - lr: 1.0000e-05\n",
      "Epoch 7/15\n",
      "461/461 [==============================] - 658s 1s/step - loss: 1.1318 - precision: 0.8063 - recall: 0.6924 - bin_accuracy: 0.8085 - auc_roc: 0.8703 - cohen_kappa: 0.5931 - val_loss: 1.2471 - val_precision: 0.9244 - val_recall: 0.4645 - val_bin_accuracy: 0.7683 - val_auc_roc: 0.8506 - val_cohen_kappa: 0.4770 - lr: 1.0000e-05\n",
      "Epoch 8/15\n",
      "461/461 [==============================] - 652s 1s/step - loss: 1.0966 - precision: 0.8126 - recall: 0.7028 - bin_accuracy: 0.8144 - auc_roc: 0.8773 - cohen_kappa: 0.6061 - val_loss: 1.1880 - val_precision: 0.9043 - val_recall: 0.5116 - val_bin_accuracy: 0.7808 - val_auc_roc: 0.8374 - val_cohen_kappa: 0.5106 - lr: 1.0000e-05\n",
      "Epoch 9/15\n",
      "461/461 [==============================] - 662s 1s/step - loss: 1.0628 - precision: 0.8237 - recall: 0.7176 - bin_accuracy: 0.8238 - auc_roc: 0.8837 - cohen_kappa: 0.6264 - val_loss: 1.1046 - val_precision: 0.8528 - val_recall: 0.6114 - val_bin_accuracy: 0.8003 - val_auc_roc: 0.8550 - val_cohen_kappa: 0.5656 - lr: 1.0000e-05\n",
      "Epoch 10/15\n",
      "461/461 [==============================] - 649s 1s/step - loss: 1.0365 - precision: 0.8297 - recall: 0.7196 - bin_accuracy: 0.8270 - auc_roc: 0.8854 - cohen_kappa: 0.6330 - val_loss: 1.0680 - val_precision: 0.8638 - val_recall: 0.6329 - val_bin_accuracy: 0.8113 - val_auc_roc: 0.8631 - val_cohen_kappa: 0.5906 - lr: 1.0000e-05\n",
      "Epoch 11/15\n",
      "461/461 [==============================] - 644s 1s/step - loss: 1.0013 - precision: 0.8353 - recall: 0.7283 - bin_accuracy: 0.8322 - auc_roc: 0.8934 - cohen_kappa: 0.6442 - val_loss: 1.1212 - val_precision: 0.9172 - val_recall: 0.5136 - val_bin_accuracy: 0.7847 - val_auc_roc: 0.8601 - val_cohen_kappa: 0.5189 - lr: 1.0000e-05\n",
      "Epoch 12/15\n",
      "461/461 [==============================] - 647s 1s/step - loss: 0.9725 - precision: 0.8361 - recall: 0.7321 - bin_accuracy: 0.8338 - auc_roc: 0.8977 - cohen_kappa: 0.6478 - val_loss: 1.0570 - val_precision: 0.8908 - val_recall: 0.5788 - val_bin_accuracy: 0.8011 - val_auc_roc: 0.8596 - val_cohen_kappa: 0.5624 - lr: 1.0000e-05\n",
      "Epoch 13/15\n",
      "461/461 [==============================] - 647s 1s/step - loss: 0.9516 - precision: 0.8344 - recall: 0.7429 - bin_accuracy: 0.8365 - auc_roc: 0.8997 - cohen_kappa: 0.6545 - val_loss: 1.0290 - val_precision: 0.8910 - val_recall: 0.5718 - val_bin_accuracy: 0.7987 - val_auc_roc: 0.8660 - val_cohen_kappa: 0.5564 - lr: 1.0000e-05\n",
      "Epoch 14/15\n",
      "461/461 [==============================] - 645s 1s/step - loss: 0.9167 - precision: 0.8423 - recall: 0.7512 - bin_accuracy: 0.8427 - auc_roc: 0.9082 - cohen_kappa: 0.6675 - val_loss: 0.9793 - val_precision: 0.8453 - val_recall: 0.6612 - val_bin_accuracy: 0.8142 - val_auc_roc: 0.8697 - val_cohen_kappa: 0.6002 - lr: 1.0000e-05\n",
      "Epoch 15/15\n",
      "461/461 [==============================] - 646s 1s/step - loss: 0.8898 - precision: 0.8491 - recall: 0.7594 - bin_accuracy: 0.8482 - auc_roc: 0.9126 - cohen_kappa: 0.6794 - val_loss: 1.0561 - val_precision: 0.9236 - val_recall: 0.5321 - val_bin_accuracy: 0.7931 - val_auc_roc: 0.8665 - val_cohen_kappa: 0.5390 - lr: 1.0000e-05\n",
      "\n",
      "-----------------------------------------------------------------------------\n",
      "Finished training the full custom VGG-19-based model.\n",
      "-----------------------------------------------------------------------------\n",
      "\n",
      "Total time for training: 13420 seconds.\n"
     ]
    }
   ],
   "source": [
    "# Capture the start time to monitor how long training takes.\n",
    "start_time = time.time()\n",
    "\n",
    "# Execute the function to train the model.\n",
    "vgg19_model, hs_upper_layers, hs_full_network = train_VGG19_binary_classification_model(\n",
    "    train_data=augmented_train_data_gen,\n",
    "    valid_data=preprocessed_valid_data_gen,\n",
    "    upper_layers_epochs=5,\n",
    "    full_network_epochs=15,\n",
    "    callbacks=custom_callbacks,\n",
    "    metrics=custom_metrics\n",
    ")\n",
    "\n",
    "# Capture the end time of training.\n",
    "end_time = time.time()\n",
    "\n",
    "# Calculate and print the time elapsed during training.\n",
    "print(f'Total time for training: {int(end_time - start_time)} seconds.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ac0fa31",
   "metadata": {
    "id": "1ac0fa31"
   },
   "source": [
    "### Training and Evaluation Insights for the Custom VGG-19 Model\n",
    "---\n",
    "\n",
    "#### Time Elapsed for Training\n",
    "The total time taken for training the model was approximately **13,420 seconds (or about 224 minutes)**. This time frame includes both the training for the upper layers and the full network.\n",
    "\n",
    "#### Metric Observations\n",
    "\n",
    "**Initial Phase (Upper Layers Training, 5 Epochs):**\n",
    "\n",
    "- **Loss**: Started at 4.6213 and reduced to 1.6244.\n",
    "\n",
    "\n",
    "- **Precision**: Increased from 0.5438 to 0.6526.\n",
    "\n",
    "\n",
    "- **Recall**: Slightly increased from 0.5487 to 0.5544.\n",
    "\n",
    "\n",
    "- **Binary Accuracy**: Increased from 0.6316 to 0.7007.\n",
    "\n",
    "\n",
    "- **AUC-ROC**: Improved from 0.6626 to 0.7457.\n",
    "\n",
    "\n",
    "- **Cohen's Kappa**: Started at 0.2362 and increased to 0.3631.\n",
    "\n",
    "\n",
    "**Full Network Training (15 Epochs):**\n",
    "\n",
    "\n",
    "- **Loss**: Started at 1.4411 and reduced to 0.8898.\n",
    "\n",
    "\n",
    "- **Precision**: Increased from 0.6801 to 0.8491.\n",
    "\n",
    "\n",
    "- **Recall**: Increased from 0.6100 to 0.7594.\n",
    "\n",
    "\n",
    "- **Binary Accuracy**: Improved from 0.7264 to 0.8482.\n",
    "\n",
    "\n",
    "- **AUC-ROC**: Improved from 0.7804 to 0.9126.\n",
    "\n",
    "\n",
    "- **Cohen's Kappa**: Started at 0.4224 and increased to 0.6794.\n",
    "\n",
    "#### Key Insights:\n",
    "\n",
    "1. **Convergence and Overfitting**: The model seems to converge as the loss decreases over epochs for both training and validation sets. No signs of overfitting are observed, which suggests that the model generalizes well on the validation set.\n",
    "\n",
    "\n",
    "2. **Precision and Recall**: Both precision and recall show a marked improvement during the full network training, indicating that the model is getting better at identifying relevant instances and minimizing false positives and negatives.\n",
    "\n",
    "\n",
    "3. **AUC-ROC**: The AUC-ROC value improves substantially, pointing to the model's increasing ability to distinguish between the classes effectively.\n",
    "\n",
    "\n",
    "4. **Cohen's Kappa**: The increase in Cohen's Kappa indicates a more reliable model as it accounts for random chance. A value closer to 1 suggests excellent reliability.\n",
    "\n",
    "\n",
    "5. **Binary Accuracy**: The binary accuracy also improves significantly, indicating that the model's overall classification performance is robust.\n",
    "\n",
    "\n",
    "6. **Learning Rate Adaptation**: The learning rate adjustments likely helped the model to escape local minima or speed up convergence, although the specific impact of this is not directly observable from the metrics.\n",
    "\n",
    "\n",
    "7. **Time Complexity**: The total time taken for training might be considered high depending on the application. It could be optimized further by various means, such as more powerful hardware or additional tuning.\n",
    "\n",
    "\n",
    "8. **High Precision in Later Epochs**: The model achieves high precision in later epochs during the full network training, making it highly reliable for positive classifications."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb2f56f0",
   "metadata": {
    "id": "bb2f56f0"
   },
   "source": [
    "### VGG-19 Model Summary Interpretation<a class=\"anchor\" id=\"vgg-19-model-summary-interpretation\"></a>\n",
    "---\n",
    "\n",
    "The `vgg19_model.summary()` function offers an exhaustive depiction of the architecture of our VGG-19-based binary classification model. The summary output encapsulates several vital pieces of information:\n",
    "\n",
    "#### **1. Layer (type)**\n",
    "This column specifies the **type of each layer** within the network. In our model, you would typically find:\n",
    "\n",
    "- **VGG-19 Base Model Layers**: These are layers from the pre-trained VGG-19 model. They include **Conv2D** layers for 2D convolutions and **MaxPool2D** layers for max-pooling, performing the initial stages of feature extraction.\n",
    "  \n",
    "  \n",
    "- **Dense**: This is the **fully connected layer** with 512 units and ReLU activation function, specifically added for our binary classification task.\n",
    "  \n",
    "  \n",
    "- **BatchNormalization**: We have added a **BatchNormalization layer** to standardize the activations from the dense layer.\n",
    "  \n",
    "  \n",
    "- **Dropout**: A **Dropout layer** with a rate of 0.2 is included to mitigate overfitting by randomly setting a fraction of input units to zero during training.\n",
    "  \n",
    "  \n",
    "- **Output**: The **final Dense layer** serves as the output layer for binary classification, using a sigmoid activation function.\n",
    "\n",
    "#### **2. Output Shape**\n",
    "This indicates the **dimensions of the output** from each layer, crucial for understanding how the feature map dimensions evolve as the data flows through the network.\n",
    "\n",
    "#### **3. Param #**\n",
    "This column shows the **number of trainable parameters** for each layer, providing a metric for the model's complexity. A high number of parameters could indicate a risk of overfitting.\n",
    "\n",
    "#### **4. Total Params**\n",
    "This accounts for the **total number of trainable and non-trainable parameters** in the model, providing an overall measure of the model's size and computational requirements.\n",
    "\n",
    "#### **5. Trainable Params**\n",
    "This specifies the **number of parameters that will be updated** during training, including both the base VGG-19 model and the additional layers added for binary classification.\n",
    "\n",
    "#### **6. Non-trainable Params**\n",
    "These are the **parameters that remain static during training**, typically the parameters from the VGG-19 base model during the initial phase of training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "583e5f37",
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    },
    "executionInfo": {
     "elapsed": 15,
     "status": "ok",
     "timestamp": 1694857213225,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
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    "id": "583e5f37",
    "outputId": "36586e20-bd1b-4b1f-a891-2a9b09074983"
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     "name": "stdout",
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     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
      "                                                                 \n",
      " block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      \n",
      "                                                                 \n",
      " block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     \n",
      "                                                                 \n",
      " block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         \n",
      "                                                                 \n",
      " block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     \n",
      "                                                                 \n",
      " block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    \n",
      "                                                                 \n",
      " block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         \n",
      "                                                                 \n",
      " block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    \n",
      "                                                                 \n",
      " block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    \n",
      "                                                                 \n",
      " block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    \n",
      "                                                                 \n",
      " block3_conv4 (Conv2D)       (None, 56, 56, 256)       590080    \n",
      "                                                                 \n",
      " block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         \n",
      "                                                                 \n",
      " block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   \n",
      "                                                                 \n",
      " block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
      "                                                                 \n",
      " block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
      "                                                                 \n",
      " block4_conv4 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
      "                                                                 \n",
      " block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         \n",
      "                                                                 \n",
      " block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_conv4 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
      "                                                                 \n",
      " block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n",
      "                                                                 \n",
      " global_max_pooling2d (Glob  (None, 512)               0         \n",
      " alMaxPooling2D)                                                 \n",
      "                                                                 \n",
      " Dense-1 (Dense)             (None, 512)               262656    \n",
      "                                                                 \n",
      " BatchNormalization-1 (Batc  (None, 512)               2048      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " Dropout-1 (Dropout)         (None, 512)               0         \n",
      "                                                                 \n",
      " Output (Dense)              (None, 1)                 513       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 20289601 (77.40 MB)\n",
      "Trainable params: 20288577 (77.39 MB)\n",
      "Non-trainable params: 1024 (4.00 KB)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# VGG Model Summary.\n",
    "vgg19_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3962d810",
   "metadata": {
    "id": "3962d810"
   },
   "source": [
    "### Observations Regarding the VGG-19 Model Summary\n",
    "---\n",
    "\n",
    "The vgg19_model obviously, is based on the VGG-19 architecture, which is a convolutional neural network (CNN) widely used in the field of computer vision. VGG-19 consists of 19 layers with learnable weights, including 16 convolutional layers and 3 fully connected layers. \n",
    "\n",
    "\n",
    "#### Layer-by-Layer Breakdown\n",
    "\n",
    "1. **Input Layer**: \n",
    "    - Shape: $224 \\times 224 \\times 3$\n",
    "    \n",
    "    - The model expects input images to have a shape of $224 \\times 224$ pixels and 3 color channels (RGB).\n",
    "\n",
    "\n",
    "\n",
    "2. **Convolutional Blocks**:\n",
    "    - **Block 1**: Two Conv2D layers with 64 filters, followed by MaxPooling. \n",
    "        \n",
    "    - **Block 2**: Two Conv2D layers with 128 filters, followed by MaxPooling.\n",
    "    \n",
    "    - **Block 3**: Four Conv2D layers with 256 filters, followed by MaxPooling.\n",
    "    \n",
    "    - **Block 4**: Four Conv2D layers with 512 filters, followed by MaxPooling.\n",
    "    \n",
    "    - **Block 5**: Four Conv2D layers with 512 filters, followed by MaxPooling.\n",
    "    \n",
    "     - All Conv2D layers use a kernel size of \\(3 \\times 3\\), which is a common choice.\n",
    "  \n",
    "  \n",
    "  \n",
    "3. **Global Max Pooling Layer**: \n",
    "    - Reduces each feature map to a single value, making the network more robust to spatial translations.\n",
    "\n",
    "\n",
    "\n",
    "4. **Fully Connected Layers**:\n",
    "    - **Dense-1**: A fully connected layer with 512 units.\n",
    "    \n",
    "    - **BatchNormalization-1**: Normalizes the activations, helping in faster and more stable training.\n",
    "    \n",
    "    - **Dropout-1**: Drops out nodes during training to prevent overfitting.\n",
    "\n",
    "\n",
    "5. **Output Layer**: \n",
    "    - A single neuron for binary classification, likely using sigmoid activation.\n",
    "\n",
    "\n",
    "#### Parameters:\n",
    "\n",
    "**Total Params**: $20,289,601$ or approximately $77.40$ MB from which $20,288,577$ or approximately $77.39$ MB where  **Trainable Params**.\n",
    "\n",
    "#### Key Observations:\n",
    "\n",
    "1. **Model Complexity**: The model has a high number of parameters, making it computationally expensive but also capable of learning complex representations.\n",
    "  \n",
    "  \n",
    "2. **Normalization and Regularization**: The use of Batch Normalization and Dropout indicates an attempt to make the model generalize better.\n",
    "\n",
    "\n",
    "\n",
    "3. **Output Shape**: The output shapes of the layers reduce in size, focusing the spatial information into fewer dimensions, aiding the classification task.\n",
    "\n",
    "\n",
    "\n",
    "4. **Trainable Parameters**: Almost all the parameters are trainable, suggesting that the model will require a sufficiently large and diverse dataset for effective training.\n",
    "\n",
    "\n",
    "\n",
    "5. **Data Preprocessing**: Given the input shape, preprocessing steps will likely include resizing images to $224 \\times 224$ and normalizing the pixel values.\n",
    "\n",
    "\n",
    "\n",
    "6. **Activation Functions**: Although not explicitly mentioned, it's likely that ReLU (Rectified Linear Unit) activations are used in the Conv2D and Dense layers, while a sigmoid activation is probably used in the output layer for binary classification.\n",
    "\n",
    "\n",
    "\n",
    "7. **Pooling Strategy**: The model uses MaxPooling after each block of Conv2D layers, which is effective in reducing the spatial dimensions of the feature maps.\n",
    "\n",
    "\n",
    "In summary, the VGG-19 model is a deep and complex model designed for high-performance image classification tasks. However, its depth and the number of parameters require significant computational resources for training and inference."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af9bc49f",
   "metadata": {
    "id": "af9bc49f"
   },
   "source": [
    "### Visualization through Plot Model<a class=\"anchor\" id=\"visualization-through-plot-model\"></a>\n",
    "---\n",
    "\n",
    "The `plot_model` function creates a graphical representation of the model architecture, which can be particularly useful for presentations or documentation. The function parameters include:\n",
    "\n",
    "- `to_file`: The filename where the plot will be saved.\n",
    "\n",
    "\n",
    "- `show_shapes`: Whether or not to display shape information.\n",
    "\n",
    "\n",
    "- `show_layer_names`: Whether or not to display layer names.\n",
    "\n",
    "This graphical visualization can provide an intuitive understanding of the model architecture, indicating how data flows through various layers and operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fdc56adb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 809,
     "status": "ok",
     "timestamp": 1694857214029,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "fdc56adb",
    "outputId": "f137d185-6d7d-429b-e87a-8debb99b1516",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the function to customize the appearance of layers and arrows.\n",
    "def customize_layer(node):\n",
    "    node.set_fillcolor('grey')\n",
    "    node.set_color('grey')\n",
    "    node.set_fontname('Arial')     # Change the font name.\n",
    "    node.set_fontsize(10)          # Adjust the font size.\n",
    "    node.set_penwidth(0.7)         # Adjust the width of the layers.\n",
    "\n",
    "\n",
    "def customize_arrow(edge):\n",
    "    edge.set_color('#73A5C6')\n",
    "    edge.set_penwidth(1)           # Increase arrow line thickness.\n",
    "    edge.set_arrowsize(0.6)        # Adjust arrowhead size.\n",
    "\n",
    "\n",
    "# Create a Dot object with smaller size.\n",
    "dot = tf.keras.utils.model_to_dot(\n",
    "    vgg19_model,\n",
    "    show_shapes=True,\n",
    "    show_layer_names=True,\n",
    "    rankdir='TB',                  # Vertical orientation.\n",
    "    expand_nested=True,            # Show nested models.\n",
    "    dpi=100,                       # Set the DPI for smaller size.\n",
    ")\n",
    "\n",
    "# Customize the appearance of layers.\n",
    "for node in dot.get_nodes():\n",
    "    customize_layer(node)\n",
    "\n",
    "# Customize the appearance of arrows.\n",
    "for edge in dot.get_edges():\n",
    "    customize_arrow(edge)\n",
    "\n",
    "# Save the plot as an image.\n",
    "dot.write_png('vgg_model.png')\n",
    "\n",
    "# Display the plot in Jupyter.\n",
    "Image('vgg_model.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c64ebdf6",
   "metadata": {
    "id": "c64ebdf6"
   },
   "source": [
    "This will generate a PNG image that outlines the model architecture, complete with the shapes of the tensors between layers and the names of each layer. This diagram can be useful for debugging and for sharing your model architecture with others."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f235ec6",
   "metadata": {
    "id": "5f235ec6"
   },
   "source": [
    "### VGG-Model Evaluation<a class=\"anchor\" id=\"vgg-model-evaluation\"></a>\n",
    "---\n",
    "\n",
    "In the **Model Evaluation** phase, our focus shifts from model training to model performance. We aim to assess how well our model generalizes to new, unseen data. Specifically, we analyze the model's behavior during training and its subsequent predictive performance.\n",
    "\n",
    "\n",
    "#### Learning Curves: A Diagnostic Tool\n",
    "---\n",
    "\n",
    "To diagnose the performance of our neural network model, we employ **learning curves** as an essential tool. Learning curves plot training and validation metrics (typically loss or accuracy) as a function of training epochs. This visualization helps us understand several crucial aspects:\n",
    "\n",
    "- **Underfitting or Overfitting**: If the model performs well on the training set but poorly on the validation set, it's likely overfitting. Conversely, if it performs poorly on both, it's probably underfitting.\n",
    "\n",
    "\n",
    "- **Model Complexity**: A steep curve might suggest that the model is complex and learns quickly, but it may also indicate overfitting.\n",
    "\n",
    "\n",
    "- **Convergence**: The point where the curves stabilize gives us an idea about how many epochs are adequate for training.\n",
    "\n",
    "\n",
    "#### Function for Plotting Learning Curves\n",
    "---\n",
    "\n",
    "To achieve this, we define a function `plot_history` that takes in the training history, number of epochs, and the metric we are interested in (either 'loss' or 'accuracy').\n",
    "\n",
    "Here's a breakdown of what the function does:\n",
    "\n",
    "- **Initialize Plot Settings**: Sets the background, figure size, and font size for the plot.\n",
    "\n",
    "\n",
    "- **Plot Training and Validation Metrics**: Plots both the training and validation curves for the specified metric.\n",
    "\n",
    "\n",
    "- **Customize Axes**: Sets the x and y-axis limits, labels, and ticks for better readability.\n",
    "\n",
    "\n",
    "- **Save Plot**: The plot is saved in SVG format for high-quality figures.\n",
    "\n",
    "\n",
    "This function will be particularly useful when it comes to visualizing the learning curves for different variations of the model, enabling us to choose the best-performing one."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09346cad",
   "metadata": {
    "id": "09346cad"
   },
   "source": [
    "### Model Evaluation and Learning Curves<a class=\"anchor\" id=\"model-evaluation-and-learning-curves\"></a>\n",
    "---\n",
    "\n",
    "In this section, we explore the performance metrics of our trained Convolutional Neural Network (CNN) model. The two primary metrics under consideration are Loss and Accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a63c3984",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 2479,
     "status": "ok",
     "timestamp": 1694858964484,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "a63c3984",
    "outputId": "a5d6603e-90ff-43dc-e375-d01a319bd1ba",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_learning_curves(history, total_epochs, metric, title, x = 1):\n",
    "\n",
    "    sns.set_style('whitegrid')\n",
    "    plt.rcParams['figure.figsize'] = [15, 8]\n",
    "    plt.rcParams['font.size'] = 16\n",
    "    plt.clf()\n",
    "\n",
    "    for hist in history:\n",
    "        plt.plot(\n",
    "            hist.history[metric],\n",
    "            label=f'Train {metric.capitalize()}',\n",
    "            linewidth=2,\n",
    "            color='#cdf6ff'\n",
    "        )\n",
    "        plt.plot(\n",
    "            hist.history[f'val_{metric}'],\n",
    "            label=f'Validation {metric.capitalize()}',\n",
    "            linewidth=2,\n",
    "            color='#50e0ff'\n",
    "        )\n",
    "\n",
    "    x_ticks = np.arange(0, total_epochs, 1)\n",
    "    plt.xticks(x_ticks)\n",
    "\n",
    "\n",
    "    # Rename x-ticks to start from 1.\n",
    "    x_tick_labels = [str(x+1) for x in x_ticks]\n",
    "    plt.xticks(x_ticks, x_tick_labels)\n",
    "\n",
    "    plt.xlim((0, total_epochs))\n",
    "    plt.ylim((0, x))\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Loss' if metric == 'loss' else 'Accuracy')\n",
    "    plt.legend(loc='upper left')\n",
    "    plt.grid(color='#f2f3f5', linestyle='-', linewidth=0.5)\n",
    "    plt.title(title, fontsize=18)\n",
    "\n",
    "    # Add a vertical line at x = 14 (best epochs weight 14th epoch)\n",
    "    plt.axvline(x=13, color='lightgray', linestyle='--', linewidth=1, label=\"Best epoch's weight\")\n",
    "    plt.show()\n",
    "\n",
    "# Plot training and validation binary accuracy across epochs.\n",
    "plot_learning_curves(\n",
    "    history=[hs_upper_layers],\n",
    "    total_epochs=5,\n",
    "    metric='bin_accuracy',\n",
    "    title='Validation Accuracy of Upper Layers Network Performance over 5 Epochs',\n",
    "    x = 1\n",
    "    )\n",
    "\n",
    "# Plot training and validation binary accuracy across epochs.\n",
    "plot_learning_curves(\n",
    "    history=[hs_full_network],\n",
    "    total_epochs=15,\n",
    "    metric='bin_accuracy',\n",
    "    title='Validation Accuracy of Full Layers Network Performance over 15 Epochs',\n",
    "    x = 1\n",
    "    )\n",
    "\n",
    "# Plot training and validation loss across epochs.\n",
    "plot_learning_curves(\n",
    "    history=[hs_upper_layers],\n",
    "    total_epochs=5,\n",
    "    metric='loss',\n",
    "    title='Validation Loss of Upper Layers Network Performance over 5 Epochs',\n",
    "    x = 5\n",
    "    )\n",
    "\n",
    "# Plot training and validation loss across epochs.\n",
    "plot_learning_curves(\n",
    "    history=[hs_full_network],\n",
    "    total_epochs=15,\n",
    "    metric='loss',\n",
    "    title='Validation Loss of Full Layers Network Performance over 15 Epochs',\n",
    "    x = 2\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f099bba4",
   "metadata": {
    "id": "f099bba4"
   },
   "source": [
    "### VGG Model Evaluation and Performance Plotting<a class=\"anchor\" id=\"vgg-model-evaluation-and-performance-plotting\"></a>\n",
    "---\n",
    "\n",
    "In this section, we focus on evaluating our **VGG-19-based binary classification model** across multiple metrics. These metrics will provide a comprehensive view of the model's performance, both during the training of the upper layers and the full network.\n",
    "\n",
    "To be more precise, this segment of the code is dedicated to evaluating our model's performance based on multiple metrics such as **Loss**, **Accuracy**, **AUC ROC**, and **Cohen's Kappa** for both the upper layers and the full network.\n",
    "\n",
    "#### Model Evaluation Metrics:\n",
    "\n",
    "1. **Loss**: Measures how well the model did on the training and validation sets. Lower values are better.\n",
    "\n",
    "\n",
    "2. **Accuracy**: Indicates the proportion of the correctly classified samples. Higher values are better.\n",
    "\n",
    "\n",
    "3. **AUC ROC**: The Area Under the Receiver Operating Characteristic curve measures the model's ability to discriminate between positive and negative classes.\n",
    "\n",
    "\n",
    "4. **Cohen's Kappa**: Measures the agreement between the predicted and actual categories. Values range from -1 to 1, with higher values indicating better agreement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e8202f96",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1694857216027,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "e8202f96",
    "outputId": "d0f63c41-f12c-4e15-d012-48b729076cc0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Upper Layers Evaluation ===\n",
      "Train Loss          : 1.62443\n",
      "Validation Loss     : 1.50525\n",
      "Train Accuracy      : 0.70071\n",
      "Validation Accuracy : 0.69560\n",
      "\n",
      "=== Full Network Evaluation ===\n",
      "Train Loss          : 0.88982\n",
      "Validation Loss     : 1.05613\n",
      "Train Accuracy      : 0.84823\n",
      "Validation Accuracy : 0.79313\n"
     ]
    }
   ],
   "source": [
    "# ================================\n",
    "# Upper Layers Performance Metrics\n",
    "# ================================\n",
    "\n",
    "print(\"=== Upper Layers Evaluation ===\")\n",
    "print(\"Train Loss          : {:.5f}\".format(hs_upper_layers.history['loss'][-1]))\n",
    "print(\"Validation Loss     : {:.5f}\".format(hs_upper_layers.history['val_loss'][-1]))\n",
    "print(\"Train Accuracy      : {:.5f}\".format(hs_upper_layers.history['bin_accuracy'][-1]))\n",
    "print(\"Validation Accuracy : {:.5f}\".format(hs_upper_layers.history['val_bin_accuracy'][-1]))\n",
    "\n",
    "# ================================\n",
    "# Full Network Performance Metrics\n",
    "# ================================\n",
    "print(\"\\n=== Full Network Evaluation ===\")\n",
    "print(\"Train Loss          : {:.5f}\".format(hs_full_network.history['loss'][-1]))\n",
    "print(\"Validation Loss     : {:.5f}\".format(hs_full_network.history['val_loss'][-1]))\n",
    "print(\"Train Accuracy      : {:.5f}\".format(hs_full_network.history['bin_accuracy'][-1]))\n",
    "print(\"Validation Accuracy : {:.5f}\".format(hs_full_network.history['val_bin_accuracy'][-1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "842f1d0e",
   "metadata": {
    "id": "842f1d0e"
   },
   "source": [
    "### VGG Model Training Insights<a class=\"anchor\" id=\"vgg-model-training-insights\"></a>\n",
    "---\n",
    "\n",
    "#### Metrics Overview\n",
    "\n",
    "1. **Training and Validation Loss**: \n",
    "    - Upper Layers: Train Loss = $1.62443 $, Validation Loss = $1.50525$\n",
    "    - Full Network: Train Loss = $0.88982 $, Validation Loss = $1.05613$ \n",
    "\n",
    "\n",
    "2. **Training and Validation Accuracy**:\n",
    "    - Upper Layers: Train Accuracy = $0.70071$ , Validation Accuracy = $ 0.69560$ \n",
    "    - Full Network: Train Accuracy = $0.84823$ , Validation Accuracy = $0.79313$\n",
    "\n",
    "#### Key Observations\n",
    "\n",
    "1. **Loss Improvement**: \n",
    "    - The training loss decreases significantly when moving from the upper layers to the full network. This indicates that the model becomes more proficient at fitting the training data.\n",
    "    - The validation loss also decreases, which is a positive indicator.\n",
    "\n",
    "\n",
    "2. **Accuracy Improvement**:\n",
    "    - Both training and validation accuracy improve when moving from the upper layers to the full network. This suggests that the deeper architecture captures the underlying patterns more effectively.\n",
    "  \n",
    "  \n",
    "3. **Generalization Gap**:\n",
    "    - The gap between training and validation accuracy is minimal, indicating better generalization.\n",
    "\n",
    "\n",
    "4. **No Signs of Overfitting**:\n",
    "    - After implementing regularization and early stopping, the model shows no signs of overfitting. The training and validation metrics are closely aligned, suggesting a well-generalized model.\n",
    "\n",
    "\n",
    "5. **Validation Performance**:\n",
    "    - The validation accuracy after full network training is around \\( 0.793 \\), which is relatively high and indicates that the model is performing well on unseen data.\n",
    "\n",
    "\n",
    "6. **Training Efficiency**:\n",
    "    - The model achieves a high training accuracy, which suggests that it is capable of learning complex representations. Moreover, it generalizes well to unseen data, as evidenced by the high validation accuracy.\n",
    "\n",
    "\n",
    "7. **Loss-Accuracy Correlation**: \n",
    "    - The training and validation losses align well with the respective accuracies, suggesting that the model is balanced and well-tuned.\n",
    "\n",
    "\n",
    "In summary, the VGG-19 model shows significant improvements in both training and validation metrics, with no signs of overfitting thanks to the implementation of regularization and early stopping. The model is well-balanced, performing admirably on both the training and validation sets. Future iterations could focus on hyperparameter tuning and exploring more advanced architectures for potential performance gains."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e70c2d4e",
   "metadata": {
    "id": "e70c2d4e"
   },
   "source": [
    "### VGG Model Evaluation on Test Data<a class=\"anchor\" id=\"model-evaluation-on-test-data\"></a>\n",
    "---\n",
    "\n",
    "In the code snippet bellow, we evaluate our trained **VGG** model on a test dataset using the `evaluate` method. The method takes the preprocessed validation data generator as an argument and computes the loss and accuracy metrics. The `verbose=2` setting provides an output without a progress bar, thereby offering a more streamlined display of evaluation results.\n",
    "\n",
    "Upon evaluating the VGG model on the test dataset, we observe that both the test loss and test accuracy metrics are presented. These metrics will provide valuable insight into how well the model is likely to perform on unseen data. A lower test loss and a higher test accuracy would indicate a more robust model. It is crucial to compare these metrics with those from the training and validation phases to assess whether the model is underfitting, overfitting, or well-balanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "VK6GOjAOVYTI",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 22727,
     "status": "ok",
     "timestamp": 1694857238751,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "VK6GOjAOVYTI",
    "outputId": "e9ee0ebf-aef7-48de-97cb-664ba70dfd1f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50/50 - 22s - loss: 0.9308 - precision: 0.9222 - recall: 0.5654 - bin_accuracy: 0.8092 - auc_roc: 0.8663 - cohen_kappa: 0.5300 - 22s/epoch - 442ms/step\n",
      "Test Loss          : 0.9308\n",
      "Test Precision     : 0.92217\n",
      "Test Recall        : 0.56536\n",
      "Test Accuracy      : 0.80916\n",
      "Test Cohen's Kappa : 0.86630\n"
     ]
    }
   ],
   "source": [
    "model_evaluation = vgg19_model.evaluate(preprocessedTestDataGen, verbose=2)\n",
    "\n",
    "# Print the test evaluation results.\n",
    "# The format method is used to round the loss and accuracy values to five decimal places.\n",
    "print(f\"Test Loss          : {model_evaluation[0]:.5f}\")\n",
    "print(f\"Test Precision     : {model_evaluation[1]:.5f}\")\n",
    "print(f\"Test Recall        : {model_evaluation[2]:.5f}\")\n",
    "print(f\"Test Accuracy      : {model_evaluation[3]:.5f}\")\n",
    "print(f\"Test Cohen's Kappa : {model_evaluation[4]:.5f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28fda19e",
   "metadata": {
    "id": "28fda19e"
   },
   "source": [
    "### VGG Model Evaluation on Test Data Insights<a class=\"anchor\" id=\"vgg-model-evaluation-on-test-data-insights\"></a>\n",
    "---\n",
    "\n",
    "#### Metrics Overview\n",
    "\n",
    "1. **Test Loss**:  $0.9308$\n",
    "\n",
    "\n",
    "2. **Test Precision**:  $0.92217$\n",
    "\n",
    "\n",
    "3. **Test Recall**:  $0.56536$\n",
    "\n",
    "\n",
    "\n",
    "4. **Test Accuracy**:  $0.80916$\n",
    "\n",
    "\n",
    "5. **Test Cohen's Kappa**:  $0.86630$\n",
    "\n",
    "#### Key Observations\n",
    "\n",
    "1. **High Precision**: \n",
    "    - The model exhibits a very high precision of $0.922$ on the test data. This means that the model correctly identifies a high proportion of positive cases out of the instances it classifies as positive.\n",
    "  \n",
    "  \n",
    "2. **Moderate Recall**: \n",
    "    - The recall is $0.565$, indicating that the model identifies approximately 56.5% of all actual positive cases. This suggests that there may be room for improvement in capturing all the potential positive cases.\n",
    "  \n",
    "  \n",
    "3. **Strong Accuracy**:\n",
    "    - With an accuracy of $0.809$, the model performs well on the test data, correctly classifying around 81% of the instances.\n",
    "  \n",
    "  \n",
    "4. **Excellent Cohen's Kappa**:\n",
    "    - A Cohen's Kappa of $0.866$ suggests excellent agreement between prediction and observation, which is a strong indicator of the model's robustness and reliability.\n",
    "\n",
    "\n",
    "5. **Balance Between Precision and Recall**:\n",
    "    - While the model excels in precision, it falls a bit short in recall. It may be worthwhile to investigate techniques that can help balance precision and recall, especially if the application scenario requires a balance of both.\n",
    "  \n",
    "  \n",
    "6. **No Signs of Overfitting**: \n",
    "    - The test metrics align well with the training and validation metrics, suggesting that the model generalizes well to new, unseen data. Regularization techniques like dropout and L1/L2 regularization have been applied, and there is no sign of overfitting.\n",
    "  \n",
    "  \n",
    "7. **Efficiency**:\n",
    "    - The model takes around 22 seconds per epoch and 442 milliseconds per step, indicating reasonable computational efficiency.\n",
    "\n",
    "\n",
    "To sum up the above, the VGG-19 model has been evaluated on test data and exhibits strong performance metrics, particularly in precision and accuracy. It also generalizes well to new data, as evidenced by the high Cohen's Kappa score. While the model excels in many aspects, there is room for improvement in recall. Future work could focus on balancing precision and recall and performing further validation to confirm these results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "49cc8593",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 19637,
     "status": "ok",
     "timestamp": 1694857541741,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "49cc8593",
    "outputId": "33f348be-3707-464a-8c2e-ed57db285506"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50/50 [==============================] - 19s 377ms/step\n"
     ]
    }
   ],
   "source": [
    "# Make predictions.\n",
    "predictions = vgg19_model.predict(preprocessedTestDataGen)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3bd6d1c",
   "metadata": {
    "id": "a3bd6d1c"
   },
   "source": [
    "### Evaluating Model Performance Across Different Study Types<a class=\"anchor\" id=\"evaluating-model-performance-across-different-study-types\"></a>\n",
    "---\n",
    "\n",
    "In the medical imaging domain, it is critical to assess the model's performance across various types of studies. This is precisely what the function `evaluate_model_by_study_type` aims to do. The function takes in two parameters: the test set (`test_set`) and the model's predictions (`predictions`). It then adds these predictions as a new column in the test set DataFrame.\n",
    "\n",
    "Subsequently, the function iterates through each unique type of study present in the test set, such as 'elbow', 'shoulder', etc., and evaluates the model's performance on that particular study type. The function calculates six different evaluation metrics: **Precision**, **Recall**, **F1 Score**, **Accuracy**, **ROC AUC**, and **Cohen's Kappa**. These metrics are crucial for understanding how well the model performs in different scenarios and are standard in the field of medical diagnostics. Each metric is rounded to five decimal places for precision.\n",
    "\n",
    "By doing so, we get a granular view of the model's efficacy and can identify which types of studies the model excels in and where it might require further optimization. This level of detail is vital for clinical applications where the consequences of a false positive or a false negative can be significant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "745b648d",
   "metadata": {
    "executionInfo": {
     "elapsed": 341,
     "status": "ok",
     "timestamp": 1694857617857,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "745b648d"
   },
   "outputs": [],
   "source": [
    "def evaluate_model_by_study_type(test_set, predictions):\n",
    "\n",
    "    # Define variable for print clarity.\n",
    "    cohen_kappa_str = \"Cohen's Kappa:\"\n",
    "\n",
    "    # Add the model predictions to the test set DataFrame.\n",
    "    test_set['predictions'] = predictions.ravel()\n",
    "\n",
    "    # Convert continuous predictions to binary based on a threshold of 0.5.\n",
    "    test_set['predictions'] = np.where(test_set.predictions >= 0.5, 1, 0)\n",
    "\n",
    "    # Retrieve a sorted list of unique study types in the test set.\n",
    "    study_types = sorted(test_set['type'].unique().tolist())\n",
    "\n",
    "    # Loop through each unique study type to evaluate the model's performance.\n",
    "    for i, st in enumerate(study_types):\n",
    "\n",
    "        # Filter the test set to only include records of the current study type.\n",
    "        df_t = test_set[test_set['type'] == st]\n",
    "\n",
    "        # Calculate various evaluation metrics for the model on the current study type.\n",
    "        precision = precision_score(df_t.label.values, df_t.predictions.values)\n",
    "        recall = recall_score(df_t.label.values, df_t.predictions.values)\n",
    "        f1_score = 2 * (precision * recall) / (precision + recall)\n",
    "        accuracy = accuracy_score(df_t.label.values, df_t.predictions.values)\n",
    "        roc_auc = roc_auc_score(df_t.label.values, df_t.predictions.values)\n",
    "        cohen_kappa = cohen_kappa_score(df_t.label.values, df_t.predictions.values)\n",
    "\n",
    "        # Print the calculated metrics.\n",
    "        print(f\"{i + 1} - Study Type: {st}\")\n",
    "        print(\"=\" * 25)\n",
    "        print(f\"{'Precision:':<15} {round(precision, 5):>10}\")\n",
    "        print(f\"{'Recall:':<15} {round(recall, 5):>10}\")\n",
    "        print(f\"{'F1 Score:':<15} {round(f1_score, 5):>10}\")\n",
    "        print(f\"{'Accuracy:':<15} {round(accuracy, 5):>10}\")\n",
    "        print(f\"{'ROC AUC:':<15} {round(roc_auc, 5):>10}\")\n",
    "        print(f\"{cohen_kappa_str:<15} {round(cohen_kappa, 5):>10}\")\n",
    "        print(\"=\" * 25)\n",
    "        print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0c04cdd9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 562,
     "status": "ok",
     "timestamp": 1694857621331,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "0c04cdd9",
    "outputId": "99e53c63-d6b8-441f-a1ee-ca24bcf37599"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 - Study Type: ELBOW\n",
      "=========================\n",
      "Precision:         0.95238\n",
      "Recall:            0.69565\n",
      "F1 Score:          0.80402\n",
      "Accuracy:          0.83226\n",
      "ROC AUC:            0.8308\n",
      "Cohen's Kappa:     0.66351\n",
      "=========================\n",
      "\n",
      "2 - Study Type: FINGER\n",
      "=========================\n",
      "Precision:         0.93913\n",
      "Recall:            0.43725\n",
      "F1 Score:          0.59669\n",
      "Accuracy:           0.7833\n",
      "ROC AUC:           0.70227\n",
      "Cohen's Kappa:     0.38853\n",
      "=========================\n",
      "\n",
      "3 - Study Type: FOREARM\n",
      "=========================\n",
      "Precision:             1.0\n",
      "Recall:            0.55629\n",
      "F1 Score:          0.71489\n",
      "Accuracy:          0.77741\n",
      "ROC AUC:           0.77815\n",
      "Cohen's Kappa:     0.55547\n",
      "=========================\n",
      "\n",
      "4 - Study Type: HAND\n",
      "=========================\n",
      "Precision:         0.87838\n",
      "Recall:            0.34392\n",
      "F1 Score:           0.4943\n",
      "Accuracy:          0.76087\n",
      "ROC AUC:           0.65535\n",
      "Cohen's Kappa:     0.34221\n",
      "=========================\n",
      "\n",
      "5 - Study Type: HUMERUS\n",
      "=========================\n",
      "Precision:         0.86325\n",
      "Recall:            0.72143\n",
      "F1 Score:          0.78599\n",
      "Accuracy:          0.80903\n",
      "ROC AUC:           0.80666\n",
      "Cohen's Kappa:     0.61606\n",
      "=========================\n",
      "\n",
      "6 - Study Type: SHOULDER\n",
      "=========================\n",
      "Precision:         0.87568\n",
      "Recall:            0.58273\n",
      "F1 Score:          0.69978\n",
      "Accuracy:          0.76311\n",
      "ROC AUC:           0.75102\n",
      "Cohen's Kappa:      0.5041\n",
      "=========================\n",
      "\n",
      "7 - Study Type: WRIST\n",
      "=========================\n",
      "Precision:         0.94872\n",
      "Recall:            0.62712\n",
      "F1 Score:           0.7551\n",
      "Accuracy:          0.81791\n",
      "ROC AUC:           0.79982\n",
      "Cohen's Kappa:     0.61955\n",
      "=========================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Execute the above created function.\n",
    "evaluate_model_by_study_type(test_set, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d210b73e",
   "metadata": {
    "id": "d210b73e"
   },
   "source": [
    "### Evaluating Model Performance Across Different Study Types Insights<a class=\"anchor\" id=\"evaluating-model-performance-across-different-study-types-insights\"></a>\n",
    "---\n",
    "\n",
    "Our analysis of the model across different anatomical study types Elbow, Finger, Forearm, Hand, Humerus, Shoulder, and Wrist—reveals several concerning trends that demand immediate attention. \n",
    "\n",
    "\n",
    "To be more precise, our VGG19 model's performance has been evaluated across seven distinct anatomical study types: Elbow, Finger, Forearm, Hand, Humerus, Shoulder, and Wrist. Each type exhibits unique performance metrics, thereby suggesting that the model's capability to generalize varies across these different categories.\n",
    "\n",
    "\n",
    "#### Detailed Metrics Insights\n",
    "\n",
    "1. **Elbow Studies**\n",
    "    - **Precision**: $0.95238$\n",
    "    - **Recall**: $0.69565$\n",
    "    - **F1 Score**: $0.80402$\n",
    "    - **Accuracy**: $0.83226$\n",
    "    - **ROC AUC**: $0.8308$\n",
    "    - **Cohen's Kappa**: $0.66351$\n",
    "\n",
    "    - **Observations**: \n",
    "        - The Elbow studies show a high precision, indicating that false positives are minimal.\n",
    "        - The Recall is relatively lower than Precision, suggesting that the model is missing a good number of true positives.\n",
    "        - The Accuracy and ROC AUC are above $0.8$, indicating good overall performance.\n",
    "        - The Cohen's Kappa score suggests moderate agreement between the predicted and observed classes.\n",
    "\n",
    "\n",
    "2. **Finger Studies**\n",
    "    - **Precision**: $0.93913$\n",
    "    - **Recall**: $0.43725$\n",
    "    - **F1 Score**: $0.59669$\n",
    "    - **Accuracy**: $0.7833$\n",
    "    - **ROC AUC**: $0.70227$\n",
    "    - **Cohen's Kappa**: $0.38853$\n",
    "\n",
    "    - **Observations**:\n",
    "        - Precision is high, but the Recall is considerably low, indicating that many positive cases are being missed.\n",
    "        - ROC AUC and Accuracy are moderate but can be improved.\n",
    "        - The Cohen's Kappa score suggests only a slight agreement, indicating that improvements are required.\n",
    "\n",
    "\n",
    "3. **Forearm Studies**\n",
    "    - **Precision**: $1.0$\n",
    "    - **Recall**: $0.55629$\n",
    "    - **F1 Score**: $0.71489$\n",
    "    - **Accuracy**: $0.77741$\n",
    "    - **ROC AUC**: $0.77815$\n",
    "    - **Cohen's Kappa**: $0.55547$\n",
    "\n",
    "    - **Observations**:\n",
    "        - The model achieves perfect Precision but fails in Recall, indicating a skewed performance.\n",
    "        - The Accuracy and ROC AUC scores suggest moderate performance.\n",
    "        - Cohen's Kappa indicates moderate agreement.\n",
    "\n",
    "\n",
    "4. **Hand Studies**\n",
    "    - **Precision**: $0.87838$\n",
    "    - **Recall**: $0.34392$\n",
    "    - **F1 Score**: $0.4943$\n",
    "    - **Accuracy**: $0.76087$\n",
    "    - **ROC AUC**: $0.65535$\n",
    "    - **Cohen's Kappa**: $0.34221$\n",
    "\n",
    "    - **Observations**:\n",
    "        - Precision is good, but Recall is extremely low, causing a low F1 score.\n",
    "        - The ROC AUC and Accuracy are the lowest among the study types, signaling room for improvement.\n",
    "        - The Cohen's Kappa score indicates slight agreement, which is concerning.\n",
    "\n",
    "\n",
    "5. **Humerus Studies**\n",
    "    - **Precision**: $0.86325$\n",
    "    - **Recall**: $0.72143$\n",
    "    - **F1 Score**: $0.78599$\n",
    "    - **Accuracy**: $0.80903$\n",
    "    - **ROC AUC**: $0.80666$\n",
    "    - **Cohen's Kappa**: $0.61606$\n",
    "\n",
    "    - **Observations**:\n",
    "        - Precision and Recall are balanced compared to other study types.\n",
    "        - The model performs well in terms of Accuracy and ROC AUC.\n",
    "        - The Cohen's Kappa score indicates moderate agreement.\n",
    "\n",
    "\n",
    "6. **Shoulder Studies**\n",
    "    - **Precision**: $0.87568$\n",
    "    - **Recall**: $0.58273$\n",
    "    - **F1 Score**: $0.69978$\n",
    "    - **Accuracy**: $0.76311$\n",
    "    - **ROC AUC**: $0.75102$\n",
    "    - **Cohen's Kappa**: $0.5041$\n",
    "\n",
    "    - **Observations**:\n",
    "        - Precision is good, but Recall is low.\n",
    "        - The model's performance, in terms of Accuracy and ROC AUC, is moderate.\n",
    "        - The Cohen's Kappa score indicates moderate agreement.\n",
    "\n",
    "\n",
    "7. **Wrist Studies**\n",
    "    - **Precision**: $0.94872$\n",
    "    - **Recall**: $0.62712$\n",
    "    - **F1 Score**: $0.7551$\n",
    "    - **Accuracy**: $0.81791$\n",
    "    - **ROC AUC**: $0.79982$\n",
    "    - **Cohen's Kappa**: $0.61955$\n",
    "\n",
    "    - **Observations**:\n",
    "        - High Precision but moderate Recall indicates missed positive cases.\n",
    "        - The Accuracy and ROC AUC are good, suggesting overall decent performance.\n",
    "        - The Cohen's Kappa score suggests moderate agreement."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae4d82e9",
   "metadata": {},
   "source": [
    "### Comparative Insights Across Study Types\n",
    "---\n",
    "\n",
    "The comparison of metrics across the seven anatomical study types reveals several patterns and disparities that warrant attention.\n",
    "\n",
    "#### 1. Precision\n",
    "\n",
    "- **Highest**: Forearm Studies with $1.0$\n",
    "- **Lowest**: Hand Studies with $0.87838$\n",
    "\n",
    "**Mention**: Forearm Studies excel in precision, indicating nearly no false positives. In contrast, Hand Studies lag, suggesting a higher rate of false positives.\n",
    "\n",
    "\n",
    "#### 2. Recall\n",
    "\n",
    "- **Highest**: Elbow Studies with $0.69565$\n",
    "- **Lowest**: Hand Studies with $0.34392$\n",
    "\n",
    "**Mention**: Elbow Studies lead in capturing true positive cases, while Hand Studies struggle significantly, missing out on a large number of actual positives.\n",
    "\n",
    "\n",
    "#### 3. F1 Score\n",
    "\n",
    "- **Highest**: Elbow Studies with $0.80402$\n",
    "- **Lowest**: Hand Studies with $0.4943$\n",
    "\n",
    "**Mention**: Elbow Studies show the best balance between precision and recall, as indicated by the highest F1 Score. Hand Studies, however, have the poorest balance, demanding immediate optimization.\n",
    "\n",
    "\n",
    "#### 4. Accuracy\n",
    "\n",
    "- **Highest**: Elbow Studies with $0.83226$\n",
    "- **Lowest**: Hand Studies with $0.76087$\n",
    "\n",
    "**Mention**: Elbow Studies exhibit the highest overall classification accuracy, whereas Hand Studies show the lowest. This implies that the model performs best when classifying elbow images and worst with hand images.\n",
    "\n",
    "\n",
    "#### 5. ROC AUC\n",
    "\n",
    "- **Highest**: Elbow Studies with $0.8308$\n",
    "- **Lowest**: Hand Studies with $0.65535$\n",
    "\n",
    "**Mention**: The model is most discriminative in Elbow Studies, as evidenced by the highest ROC AUC. Hand Studies fall short in this regard, possibly indicating an imbalance between the true positive rate and false positive rate.\n",
    "\n",
    "\n",
    "#### 6. Cohen's Kappa\n",
    "\n",
    "- **Highest**: Elbow Studies with $0.66351$\n",
    "- **Lowest**: Finger Studies with $0.38853$\n",
    "\n",
    "**Mention**: Elbow Studies show the highest level of agreement between observed and predicted classes, while Finger Studies indicate only slight agreement, raising concerns about the model's reliability for finger images."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14fd78c2",
   "metadata": {},
   "source": [
    "### Summary of Comparative Insights\n",
    "---\n",
    "\n",
    "1. **Consistent Underperformance in Hand Studies**: Across all metrics—Precision, Recall, F1 Score, Accuracy, ROC AUC, and Cohen's Kappa—Hand Studies consistently underperform. This suggests that the model struggles to generalize well for hand images and may require specialized training or feature engineering for this study type.\n",
    "\n",
    "\n",
    "2. **Forearm Studies Excel in Precision but Lag in Recall**: Despite having the highest precision, Forearm Studies suffer from low recall, indicating that while the model is excellent at identifying true negatives, it falls short in identifying true positives.\n",
    "\n",
    "\n",
    "3. **Elbow Studies as the Benchmark**: Elbow Studies generally outperform other types across most metrics, serving as a benchmark for what the model can achieve.\n",
    "\n",
    "\n",
    "4. **Variability in Cohen's Kappa**: The variance in Cohen's Kappa scores across study types suggests that the model's reliability is inconsistent and needs to be addressed.\n",
    "\n",
    "\n",
    "5. **Balancing Precision and Recall**: Most study types show a significant gap between Precision and Recall, highlighting the need for techniques like threshold adjustment or cost-sensitive learning to balance these metrics for better overall performance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7daf5fc8",
   "metadata": {
    "id": "7daf5fc8"
   },
   "source": [
    "### VGG Model Evaluation with Best Epoch Weights<a class=\"anchor\" id=\"vgg-model-evaluation-best-epoch-weights\"></a>\n",
    "---\n",
    "\n",
    "In this section, we focus on evaluating our VGG-19 model by leveraging the weights from the best-performing epoch during training. Utilizing these optimal weights aids in assessing the model's ability to generalize to unseen data effectively.\n",
    "\n",
    "\n",
    "#### **Key Steps for Model Evaluation with Best Epoch Weights**:\n",
    "\n",
    "1. **Creating a New Model Instance**: We instantiate a new model using our previously defined `build_model` function. This new instance is termed `best_model`.\n",
    "\n",
    "\n",
    "2. **Loading the Best Weights**: Through the `load_weights` method, we populate `best_model` with the weights that yielded the best performance on the validation set during training. These weights are sourced from a checkpoint file stored at a designated path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "80d5ffb6-470a-45a2-a4d6-8b711a5400de",
   "metadata": {
    "executionInfo": {
     "elapsed": 1102,
     "status": "ok",
     "timestamp": 1694859084250,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "80d5ffb6-470a-45a2-a4d6-8b711a5400de"
   },
   "outputs": [],
   "source": [
    "# Load the pre-trained model architecture.\n",
    "best_model_base, best_model = build_model(input_shape=input_shape)\n",
    "\n",
    "best_model.compile(optimizer=Adam(learning_rate=1e-4),\n",
    "                  loss='binary_crossentropy',\n",
    "                  metrics=custom_metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a127a09f",
   "metadata": {
    "executionInfo": {
     "elapsed": 359,
     "status": "ok",
     "timestamp": 1694859088223,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "a127a09f"
   },
   "outputs": [],
   "source": [
    "# Load the saved model weights with the best model (best epoch) from a file.\n",
    "best_model.load_weights(\"checkpoints/muramed_vgg19_2023-09-16_12-05-34/weights.hdf5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "900e4c0b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 41751,
     "status": "ok",
     "timestamp": 1694859133043,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "900e4c0b",
    "outputId": "43c7c22f-9169-4f87-bef3-504b060d24bf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50/50 [==============================] - 24s 487ms/step\n"
     ]
    }
   ],
   "source": [
    "# Make predictions using the loaded model.\n",
    "best_model_predictions = best_model.predict(preprocessedTestDataGen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e9bb3d96-8fd1-480b-a226-84848af12d50",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1694859133043,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "e9bb3d96-8fd1-480b-a226-84848af12d50",
    "outputId": "28728780-8c6c-4e49-cb12-e0d73e66e0a9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 - Study Type: ELBOW\n",
      "=========================\n",
      "Precision:          0.9267\n",
      "Recall:            0.76957\n",
      "F1 Score:          0.84086\n",
      "Accuracy:          0.85591\n",
      "ROC AUC:             0.855\n",
      "Cohen's Kappa:     0.71127\n",
      "=========================\n",
      "\n",
      "2 - Study Type: FINGER\n",
      "=========================\n",
      "Precision:         0.89617\n",
      "Recall:            0.66397\n",
      "F1 Score:          0.76279\n",
      "Accuracy:          0.77874\n",
      "ROC AUC:           0.78759\n",
      "Cohen's Kappa:     0.56392\n",
      "=========================\n",
      "\n",
      "3 - Study Type: FOREARM\n",
      "=========================\n",
      "Precision:         0.90476\n",
      "Recall:            0.62914\n",
      "F1 Score:          0.74219\n",
      "Accuracy:          0.79073\n",
      "ROC AUC:           0.78124\n",
      "Cohen's Kappa:      0.5619\n",
      "=========================\n",
      "\n",
      "4 - Study Type: HAND\n",
      "=========================\n",
      "Precision:         0.89474\n",
      "Recall:            0.44974\n",
      "F1 Score:          0.59859\n",
      "Accuracy:          0.79217\n",
      "ROC AUC:           0.70642\n",
      "Cohen's Kappa:     0.44643\n",
      "=========================\n",
      "\n",
      "5 - Study Type: HUMERUS\n",
      "=========================\n",
      "Precision:         0.80435\n",
      "Recall:            0.79286\n",
      "F1 Score:          0.79856\n",
      "Accuracy:          0.80556\n",
      "ROC AUC:           0.80521\n",
      "Cohen's Kappa:     0.61066\n",
      "=========================\n",
      "\n",
      "6 - Study Type: SHOULDER\n",
      "=========================\n",
      "Precision:         0.79851\n",
      "Recall:            0.76978\n",
      "F1 Score:          0.78388\n",
      "Accuracy:          0.79041\n",
      "ROC AUC:           0.79016\n",
      "Cohen's Kappa:     0.58057\n",
      "=========================\n",
      "\n",
      "7 - Study Type: WRIST\n",
      "=========================\n",
      "Precision:         0.90749\n",
      "Recall:            0.69831\n",
      "F1 Score:          0.78927\n",
      "Accuracy:          0.83308\n",
      "ROC AUC:           0.82031\n",
      "Cohen's Kappa:     0.65492\n",
      "=========================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model's performance based on predictions and ground truth labels.\n",
    "evaluate_model_by_study_type(test_set, best_model_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8539ba0a",
   "metadata": {
    "id": "8539ba0a"
   },
   "source": [
    "### Comparative Analysis of Model Performance Across Different Study Types\n",
    "---\n",
    "\n",
    "#### Original Model\n",
    "\n",
    "The original model exhibited various levels of performance across different anatomical study types—Elbow, Finger, Forearm, Hand, Humerus, Shoulder, and Wrist.\n",
    "\n",
    "#### Performance Summary\n",
    "\n",
    "- **Elbow**: High precision but moderate recall, leading to moderate F1 Score and Accuracy.  \n",
    "- **Finger**: High precision but low recall, resulting in a low F1 Score and Accuracy.  \n",
    "- **Forearm**: Perfect precision but moderate recall, leading to a moderate F1 Score and Accuracy.  \n",
    "- **Hand**: Moderate precision and low recall, leading to a low F1 Score and Accuracy.  \n",
    "- **Humerus**: Moderate precision and high recall, leading to a high F1 Score and Accuracy.  \n",
    "- **Shoulder**: Moderate precision and recall, resulting in a moderate F1 Score and Accuracy.  \n",
    "- **Wrist**: High precision but moderate recall, resulting in a moderate F1 Score and high Accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89fa7ab5",
   "metadata": {},
   "source": [
    "### Reloaded Model with Best Epoch Weights\n",
    "---\n",
    "\n",
    "#### Performance Summary\n",
    "\n",
    "- **Elbow**: High precision and recall, leading to a high F1 Score and Accuracy.  \n",
    "- **Finger**: High precision but moderate recall, resulting in a moderate F1 Score and Accuracy.  \n",
    "- **Forearm**: High precision but moderate recall, leading to a moderate F1 Score and Accuracy.  \n",
    "- **Hand**: High precision but low recall, resulting in a moderate F1 Score and Accuracy.  \n",
    "- **Humerus**: Moderate precision and high recall, leading to a high F1 Score and Accuracy.  \n",
    "- **Shoulder**: Moderate precision and high recall, resulting in a high F1 Score and Accuracy.  \n",
    "- **Wrist**: High precision and moderate recall, leading to a high F1 Score and Accuracy.\n",
    "\n",
    "\n",
    "#### Comparative Insights\n",
    "\n",
    "1. **Elbow**: The reloaded model outperforms in terms of recall and F1 Score, leading to improved Accuracy and Cohen's Kappa.\n",
    "2. **Finger**: Both models have high precision, but the reloaded model has a significantly better recall and F1 Score.\n",
    "3. **Forearm**: The reloaded model shows a decline in precision but an increase in recall, leading to a similar F1 Score.\n",
    "4. **Hand**: The reloaded model has a higher precision but still suffers from low recall, marginally improving the F1 Score.\n",
    "5. **Humerus**: Both models perform similarly, but the reloaded model has a slight edge in terms of recall.\n",
    "6. **Shoulder**: The reloaded model has similar precision but a higher recall, improving the F1 Score.\n",
    "7. **Wrist**: The reloaded model has almost the same precision but a higher recall, leading to a better F1 Score and Accuracy.\n",
    "\n",
    "\n",
    "#### Overall Observations\n",
    "\n",
    "- The reloaded model generally exhibits a better balance between precision and recall, leading to improved F1 Scores and Accuracy across most study types.\n",
    "- The reloaded model seems to have benefitted from the best epoch weights, improving its generalizability across different anatomical structures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a890fe40",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 18637,
     "status": "ok",
     "timestamp": 1694859232594,
     "user": {
      "displayName": "Dimitris Matsanganis",
      "userId": "11804405996879787534"
     },
     "user_tz": -180
    },
    "id": "a890fe40",
    "outputId": "5d8ced2b-8a04-4863-f7a6-7597453dcba4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50/50 - 18s - loss: 0.91401 - precision: 0.8716 - recall: 0.6876 - bin_accuracy: 0.8020 - auc_roc: 0.8629 - cohen_kappa: 0.5998 - 18s/epoch - 358ms/step\n",
      "\n",
      "Test Loss              : 0.91401\n",
      "Test Precision         : 0.87158\n",
      "Test Recall            : 0.68758\n",
      "Test Accuracy          : 0.80200\n",
      "Test Cohen's Kappa     : 0.86291\n"
     ]
    }
   ],
   "source": [
    "# Execute the evaluation on the validation data and store the results in 'evaluation_results'.\n",
    "evaluation_results = best_model.evaluate(preprocessedTestDataGen, verbose=2)\n",
    "\n",
    "# Print the evaluation results. Each metric is rounded to five decimal places for precision.\n",
    "print()\n",
    "print(f\"Test Loss              : {evaluation_results[0]:.5f}\")\n",
    "print(f\"Test Precision         : {evaluation_results[1]:.5f}\")\n",
    "print(f\"Test Recall            : {evaluation_results[2]:.5f}\")\n",
    "print(f\"Test Accuracy          : {evaluation_results[3]:.5f}\")\n",
    "print(f\"Test Cohen's Kappa     : {evaluation_results[4]:.5f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d270cddc",
   "metadata": {
    "id": "d270cddc"
   },
   "source": [
    "### **Evaluating Model Performance (with Best Epoch Weights) Across Different Study Types Insights**<a class=\"anchor\" id=\"evaluating-model-performance-across-different-study-types-insights\"></a>\n",
    "---\n",
    "\n",
    "The following section provides an in-depth evaluation of our VGG-19-based model's performance across various anatomical study types. This evaluation uses the best epoch weights to give us the most accurate understanding of the model's performance.\n",
    "\n",
    "\n",
    "#### **Overall Test Performance Metrics**\n",
    "\n",
    "- **Test Loss**: $0.91401$\n",
    "- **Test Precision**: $0.87158$\n",
    "- **Test Recall**: $0.68758$\n",
    "- **Test Accuracy**: $0.80200$\n",
    "- **Test Cohen's Kappa**:$0.86291$\n",
    "\n",
    "#### **Insights**\n",
    "\n",
    "1. **Loss Metrics**: The test loss value of $0.91401$ indicates a fairly optimized model, especially when considering the complexity of medical imaging data.\n",
    "  \n",
    "  \n",
    "2. **Precision**: A high precision score of $0.87158$ suggests that the model is quite reliable in its positive identifications. However, there's room for improvement to minimize false positives.\n",
    "\n",
    "\n",
    "3. **Recall**: The recall value of $0.68758$ shows that while the model is good at identifying positive cases, it may miss a significant number of actual positive cases.\n",
    "\n",
    "\n",
    "4. **Accuracy**: The accuracy score of $0.80200$ suggests that the model is fairly accurate in distinguishing between the classes, but again there is room for improvement.\n",
    "\n",
    "\n",
    "5. **Cohen's Kappa**: The Cohen's Kappa statistic of $0.86291$ indicates a substantial agreement between the predicted and actual labels, thereby reinforcing the model's reliability.\n",
    "\n",
    "\n",
    "By examining these metrics, we can identify areas for potential improvement, such as increasing the recall rate. Furthermore, these values provide a robust basis for comparing the model's performance across different study types, which will be instrumental for its real-world applicability."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06c4d155",
   "metadata": {
    "id": "06c4d155"
   },
   "source": [
    "## Conclusion<a class=\"anchor\" id=\"conclusion\"></a>\n",
    "---\n",
    "\n",
    "\n",
    "Our extensive evaluations encompassed three different models **Simple CNN**, **Advanced CNN**, and **VGG-19** across various anatomical study types such as Elbow, Finger, Forearm, Hand, Humerus, Shoulder, and Wrist provided by MURA Dataset. The objective was to pursue the robustness, accuracy, and generalizability of these models in a medical imaging context.\n",
    "\n",
    "\n",
    "### Comparative Evaluation of Model Performance\n",
    "---\n",
    "\n",
    "Upon meticulous evaluation of the test metrics using the best epoch weights, a distinct hierarchy in performance emerges among the three models under consideration: Simple CNN, Advanced CNN, and VGG-19. **Simple CNN** acts as a rudimentary baseline but is evidently limited, reflected by its test loss of **0.67427** and an accuracy of **0.61026**. These metrics underscore the need for more advanced models or additional feature engineering to make this model viable for medical applications. \n",
    "\n",
    "In contrast, **Advanced CNN** exhibits a superior performance compared to Simple CNN. It not only achieves a lower test loss of **0.59758** but also boasts a higher accuracy of **0.66938** and a commendable Cohen's Kappa score of **0.73658**. This balanced performance makes Advanced CNN a more reliable choice for medical imaging tasks that require a nuanced balance between precision and recall. \n",
    "\n",
    "However, the **VGG-19 model** surpasses both by delivering the most promising results. Despite its higher test loss of **0.91401**, the model's accuracy of **0.80200** and Cohen's Kappa score of **0.86291** are indicative of a model that is not only accurate but also robust and reliable. \n",
    "\n",
    "In conclusion, while **Simple CNN** provides a basic framework, it falls short in efficacy for medical imaging. **Advanced CNN** offers a balanced, yet still limited, alternative. The **VGG 19 model** emerges as the most reliable and robust option, particularly advantageous for critical tasks in medical imaging. This evaluation serves as a pivotal guide for future research and applications in the challenging field of medical image analysis.\n",
    "\n",
    "\n",
    "To sum up, the **VGG 19 model** appears to be the most promising for deployment in medical imaging tasks across various study types, but the **Advanced CNN** should not be overlooked as it offers a *balanced performance and could be more suitable for same real life scenarios*. \n",
    "\n",
    "We hope that this in-depth analysis serves as a robust foundation for the next steps in the development and application of neural networks in the field of medical imaging."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4eac8d6",
   "metadata": {},
   "source": [
    "### **Final Thoughts: Towards the Realization of MuraMed**\n",
    "---\n",
    "\n",
    "The comprehensive analysis and comparative evaluation of these machine learning models serve as an initiative towards our larger goal: the creation and implementation of a sophisticated medical imaging application, tentatively named **MuraMed**. While the Simple and Advanced CNN models provide foundational insights, it is the VGG-19 model that stands out as the most promising candidate for integration into a highly sensitive and reliable application like MuraMed.\n",
    "\n",
    "The performance metrics indicate that significant advancements can be achieved in medical image classification, even with existing technologies. However, they also underscore the importance of continuous innovation and adaptation to meet the stringent accuracy and reliability requirements that medical applications demand.\n",
    "\n",
    "We hope that this assignment will contribute to the global research efforts in medical image analysis, particularly in orthopedic radiology. The potential for automated, highly accurate diagnosis and treatment planning could revolutionize healthcare delivery, especially in regions lacking easy access to trained medical professionals. In this light, we envision **MuraMed** or a similar application to become a reality, bridging the gaps in medical imaging diagnosis and thereby improving global healthcare standards."
   ]
  }
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