Pixel-Boundary-Dependent Segmentation Method for Early Detection of Diabetic Retinopathy

Early and precise detection of diabetic retinopathy prevents vision impairments through computer-aided clinical procedures. Identifying the symptoms processing those by using sophisticated procedures reduces hemorrhage kind risks. The input images are influenced computer vision-based processes for segmentation classification feature extractions. In this article, a delimiting knowledge learning (DS-KL) is introduced classifying detecting exudate regions varying histograms. image identified its histogram changes from feature-dependent process. Depending on training multiple inputs with different regions, performed. This identifies infected noninfected across pixel boundaries. knowledge-learning process stores newly region correlation. recurrent improves accuracy limited errors.