RetinaXpert 👁️

RetinaXpert is a project aimed at developing a model for accurately identifying and categorizing various eye diseases based on medical images. The project utilizes a diverse set of deep neural network and machine learning models for disease recognition, with the goal of achieving high accuracy, early detection, and practical applicability in real-world scenarios.

Dataset 📊

The dataset used in RetinaXpert is a curated collection of retinal images sourced from diverse datasets, including IDRiD, Ocular Recognition, HRF, and others. It comprises the following number of images for each class:

• Cataract: 1038 images
• Glaucoma: 1007 images
• Normal: 1074 images
• Diabetic retinopathy: 1098 images

Models 🧠

Deep Neural Network Models

1. Baseline CNN Model

   • Description: A simple Convolutional Neural Network serving as a baseline for comparison.
   • Directory: /RetinaXpert/Baseline CNN Model

2. EfficientNetB3 – Transfer Learning Model

   • Description: Utilizes the EfficientNetB3 architecture with transfer learning for enhanced performance.
   • Directory: /RetinaXpert/EfficientNET Model

3. InceptionV3 - Improved Baseline CNN Model

   • Description: An upgraded version of the baseline model using the InceptionV3 architecture.
   • Directory: /RetinaXpert/InceptionV3 Model

4. ResNet Model

   • Description: Implements the ResNet architecture for deeper neural networks.
   • Directory: /RetinaXpert/ResNet Model

5. DenseNet Model

   • Description: Utilizes the DenseNet architecture for improved feature extraction.
   • Directory: /RetinaXpert/DenseNet121 Model

6. MobileNet Model

   • Description: Implements the MobileNet architecture for lightweight and efficient model design.
   • Directory: /RetinaXpert/MobileNet Model

7. VGG16 Model

   • Description: Implements the VGG16 architecture for robust feature extraction.
   • Directory: /RetinaXpert/VGG16 Model

8. Xception Model

   • Description: Utilizes the Xception architecture for improved performance.
   • Directory: /RetinaXpert/Xception Model

Machine Learning Models

1. SVM Model

   • Description: Support Vector Machine model for disease classification.
   • Directory: /RetinaXpert/SVM Model

2. Random Forest Model

   • Description: Implements a Random Forest classifier for accurate predictions.
   • Directory: /RetinaXpert/Random Forest Model

3. Decision Tree

   • Description: Utilizes a Decision Tree algorithm for disease categorization.
   • Directory: /RetinaXpert/Decision Tree Model

Introduction 🌟

Eye diseases can significantly impact vision, and early detection is crucial for effective treatment. RetinaXpert addresses this challenge by leveraging state-of-the-art models to analyze retinal images and identify conditions such as glaucoma, cataracts, diabetic retinopathy, and normal cases.

Elaborate Study PDF 📑

In the /RetinaXpert/Elaborate_Study.pdf directory, you will find a comprehensive PDF document providing an in-depth comparative analysis of the model performance. This study includes detailed insights into the strengths, weaknesses, and nuances of each model, along with visualizations of key metrics and performance on specific subsets of the dataset.

Aim and Objective 🎯

The primary goals of RetinaXpert include:

• Disease Recognition: Accurately identify and categorize eye diseases.
• High Accuracy: Achieve reliable and precise predictions.
• Early Detection: Detect eye diseases at an early stage for timely intervention.
• Efficiency: Develop models that balance accuracy and computational efficiency.
• Practical Applicability: Ensure the models are applicable in real-world clinical scenarios.
• Generalizability: Create models that generalize well to diverse datasets and patient populations.
• Adherence to Ethical Standards: Prioritize ethical considerations in the development and deployment of the models.
• Interpretability: Provide insights into model decisions for better understanding by healthcare professionals.
• Continuous Learning: Enable the model to adapt and improve over time with new data and insights.
• Validation: Rigorously validate the models to ensure their reliability and safety in a healthcare context.

Getting Started 🚀

To use the RetinaXpert models, refer to the specific model directories for detailed instructions on how to load, train, and evaluate each model. Additionally, ensure that you have the necessary dependencies installed as specified in the project's requirements.

Feedback and Contributions 🤝

RetinaXpert welcomes feedback and contributions from the community. If you encounter issues, have suggestions, or want to contribute improvements, please follow the guidelines outlined in CONTRIBUTING.md

Thank you for choosing RetinaXpert! We hope our models contribute to the advancement of eye disease diagnosis and treatment.