Automated diagnosis of age-related macular degeneration using machine learning techniques Online publication date: Mon, 02-Mar-2015
by R. Priya; P. Aruna
International Journal of Computer Applications in Technology (IJCAT), Vol. 49, No. 2, 2014
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.
Online publication date: Mon, 02-Mar-2015
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computer Applications in Technology (IJCAT):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com