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

Abstract

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