Diabetic Retinopathy Analysis System Using Image Processing and Neural Networks

Diabetic retinopathy poses a serious health problem in many regions worldwide. Addressing the medical community's need for early screening of diabetic patients and other related conditions, we have developed a computer-aided diagnostic system. This work focuses on creating an automated system that utilizes image processing techniques combined with an artificial neural network-based image classifier to analyze critical features in retinal images. The system classifies images according to disease severity levels through a multi-stage implementation: first applying preprocessing algorithms (such as contrast enhancement and noise reduction) to optimize image quality, then extracting features using techniques like blood vessel segmentation and exudate detection, and finally employing a neural network architecture (potentially using convolutional layers for feature learning) for accurate classification. This system enables physicians to diagnose and monitor diabetic retinopathy with greater precision, ultimately improving patient treatment outcomes and quality of life through automated analysis of retinal abnormalities and progression patterns.