Identification Of Glaucoma Through Fundus Images Using A deep belief network

Abstract

Glaucoma is a disease of the retina caused by highintraocular pressure. The intraocular pressure in people withglaucoma can reach 60-70mmHg. This disease is characterizedby an increasing cup to disc ratio size. Glaucoma has threelevels, namely mild with a cup to disc ratio value of 0.3-0.5, moderate with a cup to disc ratio value of 0.5-0.7 and severewith acuptodisc ratio value value above 0.7. For retinalanalysis and calculating the cup to disc ratio value taken from a fundus camera, it must be done by an expert ophthalmologist, but it takes a long time. Therefore, feature detection and automatic cup to disc ratio value calculation are expected to assist doctors in analyzing glaucoma. The data used were132 retinal fundus images consistingof66 mildg laucoma images,26moderateglaucoma images and 40 severe glaucoma images taken from the RIM-ONE dataset(http://medimrg.webs.ull.es).Pre-processing techniques like cropping, resizing, brightness, Median Filter are used for noise removal. Subsequently, feature extraction with the help of GLCM. Consequently, the method used to classify the degree of glaucoma is the Deep Belief Network. The test simulationresults obtained accuracy value of 99% with 99% of precisionand100%of recall.