retinopathy

RetinaAI - Retinal Image Analyzer

An AI-powered application for analyzing retinal images to detect potential health issues, including diabetic retinopathy.

Overview

RetinaAI uses a Vision Transformer (ViT) model trained on retinal images to classify and analyze retinal health conditions. The application provides detailed explanations for each analysis.

Features

• Image Classification: Analyzes retinal images for potential health issues
• Confidence Scoring: Provides confidence scores for predictions
• Easy-to-Use Interface: Intuitive Gradio interface for quick analysis
• Professional Explanations: Detailed analysis results with clinical considerations

Supported Conditions

The model is trained to detect:

• No Diabetic Retinopathy (No_DR)
• Mild Diabetic Retinopathy (Mild)
• Moderate Diabetic Retinopathy (Moderate)
• Severe Diabetic Retinopathy (Severe)
• Proliferative Diabetic Retinopathy (Proliferate_DR)

Installation

Local Setup

# Install dependencies
pip install -r requirements.txt

Requirements

• Python 3.8+
• PyTorch 2.1.2
• Transformers 4.36.2
• Gradio 4.24.0

Usage

Run Locally

python app.py

The application will launch a web interface at http://localhost:7860

Model Details

• Base Model: Google Vision Transformer (vit-base-patch16-224)
• Fine-tuning Data: Retinal images classified into 5 severity levels
• Framework: PyTorch + Hugging Face Transformers

How It Works

1. Upload a retinal image (JPG, PNG, etc.)
2. The model processes the image through the Vision Transformer
3. Classification results are generated with confidence scores
4. Detailed explanation is provided based on the classification

Important Disclaimer

⚠️ Medical Disclaimer: This tool is designed for educational and research purposes only. It should NOT be used as a substitute for professional medical advice, diagnosis, or treatment. Always consult with a qualified ophthalmologist or eye care professional for accurate diagnosis and treatment of retinal conditions.

Dataset

The model was trained on the Diabetic Retinopathy Detection dataset with images classified into the following categories:

• Mild
• Moderate
• No_DR
• Proliferate_DR
• Severe

Performance

The model achieves high accuracy on the validation dataset. For specific metrics and detailed performance information, please refer to the project documentation.

Authors

RetinaAI Development Team

License

This project is provided as-is for research and educational purposes.

Citation

If you use this model in your research, please cite:

@article{retinaai2024,
  title={RetinaAI: Diabetic Retinopathy Detection using Vision Transformers},
  author={Your Name},
  year={2024}
}

Contact & Support

For issues, questions, or suggestions, please create an issue in the repository.

Acknowledgments

• Hugging Face for the transformers library
• Google for the Vision Transformer model
• [Dataset source/citation if applicable]

---

Last Updated: April 2024
Version: 1.0.0