Explainable Diabetic Retinopathy Detection System

Overview

An end-to-end system for detecting diabetic retinopathy in retinal images with built-in explainability features. The system combines deep learning for detection with advanced visualization and natural language explanations to provide transparent and interpretable results.

Key Features

• Diabetic Retinopathy detection using deep learning models
• Model explainability using XAIN (Explainable AI Network)
• Grad-CAM visualization for highlighting critical image regions
• Custom NLP model for generating natural language explanations
• Lightweight, user-friendly frontend interface

Technical Architecture

• Backend: Python-based ML pipeline
• Deep Learning Model: ResNet50 for DR classification
• Explainability Tools:
  • XAIN implementation
  • Grad-CAM visualization
  • Custom NLP explanation generator
• Frontend: Lightweight web interface

Installation & Setup

# Clone the repository
git clone [your-repo-url]

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # Linux/Mac
# or
.venv\Scripts\activate  # Windows

# Install dependencies
pip install -r requirements.txt

Usage

[Add specific instructions on how to use your application]

Model Performance

• Classification Accuracy: [Add metrics]
• Explanation Accuracy: [Add metrics]
• Processing Time: [Add performance details]

License

[Add your license information]