Retinal Analysis and Calculating Cup to Disc Ratio Value Using Deep Learning Models

Abstract

Glaucoma is a disease of the retina caused by high intraocular pressure. The intraocular pressure in people with glaucoma can reach 60-70 mm Hg. This disease is characterized by an increasing cup to disc ratio size. Glaucoma has three levels, 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 severe with a cup to disc ratio value above 0.7. For retinal analysis 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 were 132 retinal fundus images consisting of 66 mild glaucoma images, 26 moderate glaucoma 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 simulation results obtained accuracy value of 99% with 99% of precision and