README.md

AI Diabetic Retinopathy Detection

An AI-powered application for detecting diabetic retinopathy (DR) from Optical Coherence Tomography (OCT) images using deep learning and Grad-CAM visualization.

🏥 What is Diabetic Retinopathy?

Diabetic retinopathy is a diabetes complication that affects the eyes. It's caused by damage to the blood vessels of the light-sensitive tissue at the back of the eye (retina). Early detection is crucial for preventing vision loss.

🚀 Features

• AI Classification: Uses a pre-trained ResNet-50 model to classify OCT images as DR (Diabetic Retinopathy) or NoDR (No Diabetic Retinopathy)
• Grad-CAM Visualization: Shows which areas of the image the AI focuses on for diagnosis
• Confidence Scoring: Provides probability scores for predictions
• Image Storage: Automatically saves analyzed images with timestamps
• Web Interface: User-friendly Gradio web interface

📋 Requirements

• Python 3.8 or higher
• PyTorch 2.0+
• CUDA-compatible GPU (optional, for faster inference)

🛠️ Installation

1. Clone or download this repository

   git clone <repository-url>
   cd Deep_Learning_for_Ophthalmologist
   

2. Create a virtual environment (recommended)

   python -m venv venv
   
   # On Windows:
   venv\Scripts\activate
   
   # On macOS/Linux:
   source venv/bin/activate
   

3. Install dependencies

   pip install -r requirements.txt
   

🎯 Usage

1. Start the application

   python app.py
   

2. Open your web browser and navigate to the URL shown in the terminal (usually http://127.0.0.1:7860)

3. Upload an OCT image by clicking the upload area or dragging and dropping an image file

4. View results:

   • The AI will analyze the image and show the classification result
   • A Grad-CAM heatmap will highlight areas of interest
   • The prediction and confidence score will be displayed
   • The analyzed image will be automatically saved to the saved_predictions folder

📁 File Structure

Deep_Learning_for_Ophthalmologist/
├── app.py                          # Main application file
├── requirements.txt                # Python dependencies
├── resnet50_dr_classifier.pth     # Pre-trained model weights
├── README.md                      # This file
└── saved_predictions/             # Folder for saved analyzed images

🔬 How It Works

1. Image Preprocessing: OCT images are resized to 224x224 pixels and normalized
2. AI Analysis: A ResNet-50 model processes the image to classify DR vs NoDR
3. Grad-CAM: Generates a heatmap showing which image regions influenced the AI's decision
4. Results: Displays classification, confidence score, and visual heatmap

📊 Model Information

• Architecture: ResNet-50 with modified final layer for binary classification
• Training: Pre-trained on OCT image dataset
• Classes: 2 (DR - Diabetic Retinopathy, NoDR - No Diabetic Retinopathy)
• Input: 224x224 RGB images
• Output: Binary classification with confidence scores

⚠️ Important Notes

• Medical Disclaimer: This tool is for research and educational purposes only. It should not be used for actual medical diagnosis without proper validation and clinical oversight.
• Image Quality: For best results, use high-quality OCT images with good contrast and resolution
• Model Limitations: The model's accuracy depends on the quality and characteristics of the training data

🐛 Troubleshooting

• CUDA errors: The app runs on CPU by default. If you have GPU issues, ensure PyTorch is installed correctly
• Memory issues: Large images may cause memory problems. The app automatically resizes images to 224x224
• Model loading errors: Ensure resnet50_dr_classifier.pth is in the same directory as app.py

🤝 Contributing

Contributions are welcome! Please feel free to submit issues, feature requests, or pull requests.

📄 License

This project is for educational and research purposes. Please ensure compliance with relevant regulations when using medical imaging data.

🔗 References

• PyTorch
• Gradio
• Grad-CAM
• ResNet Paper

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Note: This application is designed for research and educational purposes in ophthalmology and medical AI. Always consult with qualified healthcare professionals for actual medical diagnosis and treatment decisions.