This project is a Retinal OCT (Optical Coherence Tomography) Analysis Platform built with Streamlit and TensorFlow. It allows users to upload OCT images and automatically classify them into CNV, DME, Drusen, or Normal. The platform also provides detailed insights and recommendations for each retinal condition.
- Automated Image Classification: Detects CNV, DME, Drusen, and Normal retina.
- Interactive Dashboard: Built with Streamlit for easy use.
- Detailed Recommendations: Provides information and guidance based on predicted disease.
- High Accuracy: Trained on a verified dataset of 84,495 OCT images.
The dataset contains images from multiple sources, verified by ophthalmologists and retinal specialists:
- Shiley Eye Institute
- California Retinal Research Foundation
- Medical Center Ophthalmology Associates
- Shanghai First People’s Hospital
- Beijing Tongren Eye Center
- Clone the repository:
git clone https://github.com/inameatl/Human_Eye_Disease_Prediction.git
cd Human_Eye_Disease_Prediction
python -m venv venv
source venv/bin/activate # Linux/macOS
venv\Scripts\activate # Windows
pip install -r requirements.txt
streamlit run app.py
# Human_Eye_Disease_Prediction_with_react