Performance analysis of screening diabetic retinopathy

Abstract

This study presents a new method for screening Diabetic Retinopathy (DR), is the leading ophthalmic pathological cause of blindness among people of working age in developed countries. The first manifestations of DR are tiny capillary dilations known as Microaneurysms (MA) and Exudates. It may provide an early indication of the risk of the Type –I Diabetes. The various features of the images of the Retinal Vessels are used to indicate the different MA’s and Exudates disease processes. Neural Networks and k-means clustering provide significant benefits in medical research. This Proposed work deals DR with Segmentation and Classification algorithms for the analysis of Retina images. This effectiveness and robustness, together with its simplicity make this Optimized analysis for being integrated into a complete screening system for early DR detection. It proposes an Optimized Soft Computing technique approach for screening the Diabetic retinopathy.