Transforming Diabetic Retinopathy Detection: Advanced Strategies with Convolutional Neural Networks

Abstract

Diabetic retinopathy is an aspect of diabetes which produces vascular abnormalities that might result in blindness. Since the symptoms with this illness are lasting, detection at an early stage is crucial because untreated eye disease may lead to blindness. The detection of microaneurysms using digital color fundus images is an essential initial step in automated diabetic retinopathy testing. Five different DR stages or classes (proliferative diabetic retinopathy), typical, mild, moderate, serious, and PDR, are typically assessed by expert doctors to determine this disastrous disorder. Manually diagnosis (by physicians) for such ailment requires time and is vulnerable to inaccuracy. There have been several computer vision-based algorithms created for autonomously recognizing DR and its different stages from retina scans. The results of this research utilize fundus-colored images to provide a computational method for recognizing and categorizing the condition via a convolution neural network.