AN AI-ENABLED DIAGNOSTIC SYSTEM FOR DIABETIC EYE DISEASE DETECTION USING DEEP NEURAL NETWORKS

Abstract

Timely detection of diabetic retinopathy and other diabetic ocular conditions is crucial, since they significantly contribute to worldwide visual impairment. This research introduces DeepDiabetic, a method using deep neural networks to independently identify diabetic eye problems in retinal pictures. DeepDiabetic employs sophisticated convolutional neural network (CNN) architectures to rapidly and accurately categorize and identify disease stages via the analysis of fundus pictures. The study findings demonstrate that the system proficiently differentiates diabetic eye diseases, possibly expediting patient help and enhancing overall outcomes. The suggested method provides a scalable, non-invasive, and economical tool to aid ophthalmologists in clinical decision-making and the implementation of comprehensive diabetic eye screening programs.