Explainable AI model for early detection of retinal diseases using deep learning. Helping healthcare professionals diagnose faster with transparent, interpretable results.
Early diagnosis saves vision, but traditional methods have limitations
Manual diagnosis requires extensive time from skilled ophthalmologists
Different specialists may reach different conclusions for the same case
Late diagnosis can lead to permanent vision damage
An automated and explainable AI system that assists healthcare professionals in detecting retinal anomalies accurately and interpretably, making diagnosis faster, more transparent, and educational.
Advanced AI capabilities for comprehensive retinal analysis
Simultaneously detects multiple retinal conditions with confidence scores using ResNet50 transfer learning.
GPT-4 integration converts technical predictions into human-readable medical explanations.
Visual heatmaps show exactly which areas of the retina influenced the AI's decision.
Easy-to-use web interface for uploading fundus images and receiving instant analysis.
Fast inference with image validation, preprocessing, and disease prediction in seconds.
Trained on 3,285 fundus images from Kaggle and public medical datasets.
Built with cutting-edge AI and web technologies
ResNet50 with transfer learning
4th layer retrained for retinal disease classification
Multi-label classification for 7 conditions
Grad-CAM heatmaps + GPT-4 explanations
See how our AI explains its diagnostic decisions
"The analysis reveals significant signs of diabetic retinopathy with 87% confidence. Key indicators include microaneurysms, hemorrhages, and exudates visible in the retinal blood vessels. The Grad-CAM heatmap highlights areas of concern around the optic disc and macula. Early intervention is recommended to prevent progression to proliferative diabetic retinopathy."
Easy installation and setup process
Download the project from GitHub
Set up Python environment and packages
Start the web interface
git clone https://github.com/username/RetinalAnomalyDetection.git
cd RetinalAnomalyDetection
# Python Version: 3.12.3
pip install -r requirements.txt
python main.py
Experience the future of retinal disease detection with explainable AI
Open source • Free to use • Educational purpose