European Journal of Molecular & Clinical Medicine

Abstract

Diabetic retinopathy is an eye-related neurological disorder, the diabetic patient eye damaged by blood vessel in the retina area of the eye. Computational methodology is a proper way for detecting and predicting the diabetic retinopathy disease. The aim is to identify and detect the Diabetic Retinopathy, so this present work focusses on detection of Diabetic Retinopathy. This work proposed the novel WMD-MSVM -Weighted Mahalanobis Distance based Multiclass Support Vector Machine oriented; upon Diabetic Retinopathy diagnosis system for the purpose of feature selection, also ROI extraction method being utilized to fetch features from Diabetic Retinopathy images. From the results, it is clear that the performance of WMD-MSVM on instance selected training dataset yields improved detection accuracy compared with the performance of WMD-MSVM on full-training-dataset. There is an improvement of around 1% of detection accuracy in case instance selected dataset. This proposed work is benefit for diabetic patients to gain the proper treatment by physicians at an early stage for Diabetic Retinopathy. This computational approach to detect   the diabetic which results the best solutions for ophthalmology. The diabetic image analysis and machine learning approach considered as a challenging research area that aims to provide a computational approach to assist in the early diagnosis and detection of Diabetic Retinopathy problems.