Diabetic Retinopathy Detection Using Image Processing Techniques in Python

This project implements an automated system to detect diabetic retinopathy (DR) from retinal fundus images using classical image processing techniques in Python. The code extracts key features such as blood vessels and exudates (bright lesions) from retinal images. By analyzing the number and area of white spots (exudates), the system decides whether signs of diabetic retinopathy are present. The pipeline uses OpenCV and scikit-image libraries for tasks such as contrast enhancement, morphological filtering, edge detection, and blob analysis. The system was tested on 10 retinal images with 100% accuracy (5 DR-positive and 5 DR-negative). This work demonstrates the potential of simple rule-based image analysis in early disease detection.