Automated Detection of Diabetic Retinopathy Using Weighted Support Vector Machines

Abstract

Diabetic retinopathy is a complication of the eye caused by damage to the retinal cells due to prolonged suffering from diabetes mellitus and may lead to irreversible vision impairment in middle-age adults. The proposed algorithm detects the presence of Diabetic Retinopathy (DR) by segmentation of vital morphological features like Optic Disc, Fovea, blood vessels, and abnormalities like hemorrhages, exudates and neovascularization. The images are then classified using Support Vector Machines, based on data points in a multi-dimensional feature space. The proposed method is tested on 140 images from the Messidor database, from which 75 images are used to train an SVM model and the remaining 65 are used as inputs to the classifier.