IDENTIFICATION OF DIABETIC MACULOPATHY STAGES USING FUNDUS IMAGES

Diabetes mellitus is a major cause of visual impairment and blindness. Almost all patients with type 1 diabetes and over 60% of patients with type 2 diabetes will develop some degree of retinopathy after having diabetes for twenty years. The purpose of this work is to present a computer-based intelligent system for identification of clinically significant, non-clinically significant maculopathy and normal fundus eye images. The features were extracted from the raw fundus images using morphological image processing technique and fed to the two classifiers, the feed-forward architecture artificial neural network (ANN) and probabilistic neural network (PNN), for comparison. 90 subjects consisting of three different types of maculopathy eye disease conditions have been studied in this work.