Pneumonia Detection using Convolutional Neural Network

This repository contains a project focused on using a Convolutional Neural Network (CNN) to detect pneumonia from chest X-ray images. The dataset used for training and validation consists of labeled images indicating whether pneumonia is present or not.

Project Overview

Pneumonia is a serious lung infection that primarily affects the alveoli, causing inflammation and fluid build-up. Early detection is crucial for effective treatment, and this project aims to automate the process using deep learning.

The notebook provides a comprehensive guide to building, training, and evaluating a CNN for the classification of X-ray images as either showing signs of pneumonia or being normal.

Key Features

• Dataset: Chest X-ray images used for training and validation.
• Preprocessing: Images are resized, normalized, and augmented to improve the generalization of the model.
• Model Architecture: A CNN model is designed from scratch or using a pre-trained model (details within the notebook).
• Training: The model is trained on a labeled dataset using supervised learning techniques.
• Evaluation: Performance is evaluated based on accuracy, precision, recall, and F1-score, among others.

Dataset

The dataset used in this project is publicly available and consists of labeled X-ray images categorized into two classes:

1. Normal - X-ray images showing no signs of pneumonia.
2. Pneumonia - X-ray images showing signs of pneumonia.

The dataset can be downloaded from the Kaggle Pneumonia Dataset.

Requirements

To run the notebook, you will need the following dependencies:

• Python 3.x
• Jupyter Notebook
• TensorFlow / Keras
• NumPy
• Matplotlib
• Scikit-learn
• OpenCV (for image processing)
• PIL (Python Imaging Library)

Install the required libraries by running:

pip install -r requirements.txt

Project Structure

• Pneumonia_Detection_using_Conv_Neural_Network.ipynb: Main notebook containing the implementation of the CNN model.
• data/: Folder containing the dataset (ensure this is correctly structured before running the notebook).
• models/: Folder where trained models and checkpoints will be saved.
• README.md: Project documentation.

How to Run

1. Clone the repository:

git clone https://github.com/https-404/pneumonia-detection.git
cd pneumonia-detection

2. Install dependencies:

pip install -r requirements.txt

3. Download the dataset and place it in the data/ directory.

4. Open the Jupyter notebook and run the cells to preprocess data, build the CNN model, and train the model.

jupyter notebook Bytewise_Final_Areesh_Pneumonia_Detection_using_Conv_Neural_Network.ipynb

5. Monitor the training process and evaluate the model performance on the validation set.

Results

After training, the model achieves good accuracy in detecting pneumonia from X-ray images. Results such as accuracy, confusion matrix, and classification report are available in the notebook.

Example Results

• Accuracy: 85%
• Precision: X
• Recall: X
• F1-Score: X

(The exact metrics will depend on the final training run.)

Future Work

• Further improve the accuracy by experimenting with more advanced CNN architectures.
• Add real-time prediction using a Flask or Django-based web application.
• Explore the possibility of transfer learning using pre-trained models like VGG16, ResNet, etc.

License

This project is licensed under the MIT License - see the LICENSE file for details.