An Approach of Abnormality Detection for Diabetic Retinopathy using ANN SVM

Glaucoma is the diagnosis given to a group of ocular conditions that contribute to the loss of retinal nerve fibers with a corresponding loss of vision. Glaucoma is the major cause of blindness in people above the age of 40. The Intra Ocular Pressure (IOP) increases because of the malfunction of the drainage structure of the eyes leading to Glaucoma. In this paper three methods-Gray Level Difference Method using ANN classifier, Stochastic watershed method using SVM classifier and Pearson R Correlation Method are proposed, which automatically detect Glaucoma disease in the human eye from the fundus database images. The three strategies utilized for retinal abnormality identification are looked at in light of the execution measurements- exactness and blunder rate. The SVM classifier gives more precise outcomes contrasted with other two procedures.