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Comparison of Computational Intelligent Technique for Detecting Age-Related Macular Degeneration

Published on February 2013 by R. Priya, P. Aruna, D. Thenmozhi
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International Conference on Research Trends in Computer Technologies 2013
Foundation of Computer Science USA
ICRTCT - Number 1
February 2013
Authors: R. Priya, P. Aruna, D. Thenmozhi
f890fa9b-6cee-45d9-a085-69a69e6c5274

R. Priya, P. Aruna, D. Thenmozhi . Comparison of Computational Intelligent Technique for Detecting Age-Related Macular Degeneration. International Conference on Research Trends in Computer Technologies 2013. ICRTCT, 1 (February 2013), 30-33.

@article{
author = { R. Priya, P. Aruna, D. Thenmozhi },
title = { Comparison of Computational Intelligent Technique for Detecting Age-Related Macular Degeneration },
journal = { International Conference on Research Trends in Computer Technologies 2013 },
issue_date = { February 2013 },
volume = { ICRTCT },
number = { 1 },
month = { February },
year = { 2013 },
issn = 0975-8887,
pages = { 30-33 },
numpages = 4,
url = { /proceedings/icrtct/number1/10805-1017/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Research Trends in Computer Technologies 2013
%A R. Priya
%A P. Aruna
%A D. Thenmozhi
%T Comparison of Computational Intelligent Technique for Detecting Age-Related Macular Degeneration
%J International Conference on Research Trends in Computer Technologies 2013
%@ 0975-8887
%V ICRTCT
%N 1
%P 30-33
%D 2013
%I International Journal of Computer Applications
Abstract

Age-related macular degeneration is an eye disease, which leads to loss of eye in the elderly, due to degeneration of macula in retina. The disease comes within the one of the two types namely, 1) Dry ARMD and 2) Wet ARMD. The purpose of this paper is to diagnose the disease ARMD and classify the types it belongs to. The extent of the disease spread can be identified by extracting the features of the retina. Detection of the disease is done using Probabilistic Neural Network (PNN) classifier. The accuracy of the proposed system is 78%.

References
  1. D. Jayanthi, N. Devi, S. SwarnaParvathi, "Automatic Diagnosis of Retinal Diseases from Color Retinal Images", International Journal of Computer Science and Information Security, Vol. 7, pp:1, 2010.
  2. Ziyang Liang, Damon W. K. Wong, Jiang Liu, Kap Luk Chan, Tien Yin Wong, " Towards automatic detection of age-related macular degeneration in retinal fundus images", 32nd Annual International Conference of the IEEE, pp:4100-4103, 2010
  3. D. E. Freund, N. Bressler, P. Burlina, "Automated Detection of Drusen in the Macula", ISBI, pp. 61-64, 2009.
  4. MohdHanafi Ahmad Hijazi, Francs Coenen, YalinZheng, "A Histogram Approach for the Screening of Age-Related Macular Degeneration", Ophthalmology Research Unit.
  5. Adam Hoover and Michael Goldbaum, "Locating the Optic Nerve in a Retinal Image using the Fuzzy Convergence of the Blood Vessels", IEEE Transactions on Medical Imaging, Vol. 22, 2003.
  6. R. Priya and Dr. P. Aruna, "Automated Diagnosis Of Age-Related Macular Degeneration From Color Retinal Fundus Images", International Conference on Electronics Computer Technology(ICECT), 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Macula Retina Probabilistic Neural Network Accuracy Sensitivity Specificity