A Novel Approach for Design and Analysis of Diabetic Retinopathy Glaucoma Detection Using Cup to Disk Ration and ANN. (2015)

• Record Type: Journal Article
• Authors: Rao, P.V.; Gayathri, R.; Sunitha, R.
• Is Part Of: Procedia materials science. Volume 10(2015)Supplement
• Page Start: 446
• Page End: 454
• Publication Date: 2015
• Subjects: Z-Score Normalization -- Cup-to-Disc -- Symlets -- Biorthogonal and Daubechies wavelets -- MLP-BP ANN; Materials science -- Congresses; Materials science -- Periodicals; Materials science; Conference proceedings; Periodicals; 620.11
• DOI: 10.1016/j.mspro.2015.06.080
• Languages: English
• ISSNs: 2211-8128

Abstract

Abstract: Glaucoma is an eye disease which damages the optic nerve of the eye and becomes severe over a period of time. It is caused due to buildup of pressure inside the eye. Glaucoma tends to be inherited and may not show up until later in life. The detection of glaucomatous progression is one of the most important and most challenging aspects of primary open angle glaucoma (OAG) management. It is caused due to buildup of pressure inside the eye. The detection of glaucomatous progression is one of the most important and most challenging aspects of primary open angle glaucoma (OAG) management. The early detection of glaucoma is an important in human life in order to enable appropriate monitoring, treatment and to minimize the risk of irreversible visual field loss. Although advances in ocular imaging offer the potential for earlier diagnosis, the best method is to involve a combination of information from structural and functional tests. In this proposed method both structural and energy features are considered, then analyzed to classify as glaucomatous image. Energy distribution over cup to disk ration were applied to find these important texture energy features. Finally extracted energy features are applied to Multilayer Perceptron (MLP) and Back Propagation (BP) neural network for effective classification by considering normal subject's extracted energy features. Naive Bayes classifies the images in the database with the accuracy of 89.6%. MLP-BP Artificial Neural Network (ANN) algorithm classifies the images in the database with the accuracy of 90.6%.