Advanced Classification Technique for Diabetic Eye Disorders

This project presents the development and implementation of an AI-powered system for the early detection and classification of diabetic eye conditions using retinal (fundus) images. With increasing cases of diabetic retinopathy and other vision-threatening diseases like glaucoma and cataracts, There is an increasing demand for quick, dependable,and cost- effective screening tools. Manual diagnosis is often time- consuming and prone to variability. Hence, the integration of automated solutions using Deep Learning (DL) and Machine Learning (ML) can enhance diagnostic accuracy and accessibility. The system follows a structured pipeline starting with image preprocessing to enhance input quality and reduce noise. This is followed by the use of convolutional neural networks for feature extraction. (CNNs) to capture disease-specific patterns. A hybrid classification module then maps these features to specific disease types and stages, utilizing both deep learning and traditional ML classifier. To improve model robustness, the system is trained on publicly available datasets of annotated fundus images. It also incorporates additional patient parameters like age and medical history where available. The model is evaluated based on accuracy, sensitivity, and specificity, showing promising results in differentiating between normal eyes, early-stage, and advanced cases of diabetic retinopathy, as well as other eye conditions. The final output not only detects the presence of a condition but also classifies its severity. This implementation aims to support healthcare professionals in making quicker and more accurate decisions and provides a scalable tool for use in telemedicine and community health screenings.