Title: Automated diagnosis of age-related macular degeneration using machine learning techniques

Authors: R. Priya; P. Aruna

Addresses: Department of Computer Science & Engineering, Annamalai University, Annamalai Nagar, Chidambaram 608002, Tamil Nadu, India ' Department of Computer Science & Engineering, Annamalai University, Annamalai Nagar, Chidambaram 608002, Tamil Nadu, India

Abstract: Age-related macular (ARM) degeneration is an eye disease, that gradually degrades the macula, a part of the retina, which is responsible for central vision. It occurs in one of the two types, dry and wet age-related macular degeneration. The purpose of this paper is to diagnose the retinal disease age-related macular degeneration. An automated approach is proposed to help in the early detection of age-related macular degeneration using three models and their performances are compared. The amount of the disease spread in the retina can be identified by extracting the features of the retina. Detection of age-related macular degeneration disease has been done using probabilistic neural network (PNN), Bayesian classification and support vector machine (SVM) and the two types of age-related macular degeneration are classified and diagnosed successfully. The results show that SVM achieves a higher performance measure than probabilistic neural network and Bayes classification.

Keywords: fundus images; retina; PNN; probabilistic neural networks; SVM; support vector machine; Bayesian classification; sensitivity; specificity; automated diagnosis; age-related macular degeneration; machine learning; eye disease.

DOI: 10.1504/IJCAT.2014.060527

International Journal of Computer Applications in Technology, 2014 Vol.49 No.2, pp.157 - 165

Available online: 18 Apr 2014 *

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