Bringing AI to Automatic Diagnosis of Diabetic Retinopathy using Image Processing and Machine Learning Technique

Diabetic Retinopathy (DR) is a severe complication of diabetes that can lead to vision loss if not detected early. This project focuses on developing an AI-based system for automatic detection and multi-class diagnosis of Diabetic Retinopathy using image processing and machine learning techniques. By leveraging a Python-based web application, the system can process retinal images, extract meaningful features, and classify the severity of DR into multiple stages. The use of advanced Convolutional Neural Networks (CNNs) ensures high accuracy and reliability in identifying the disease at an early stage, making it possible to assist doctors in timely diagnosis. The system uses a Diabetic Retinopathy dataset for training and validation. Images are pre-processed to enhance features such as blood vessels, exudates, and microaneurysms, which are critical indicators of the disease. The CNN model is trained to recognize patterns corresponding to different stages of DR, ensuring efficient and automated diagnosis. This reduces the dependency on manual screening, which is time-consuming and prone to human error, providing a scalable solution for mass screening in healthcare systems. The proposed approach not only offers accurate classification of DR severity but also provides a user-friendly interface for medical professionals to upload images, visualize predictions, and monitor patient progress. By combining image processing and machine learning, this project aims to improve early detection rates, assist ophthalmologists in decision-making, and contribute to better patient outcomes in diabetic care.