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A Survey of the Prevalence and Different Techniques for Glaucomatous Image Classification

Published on December 2014 by Nilima S. Patil, R. B. Wagh
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National Conference on Emerging Trends in Information Technology
Foundation of Computer Science USA
NCETIT - Number 1
December 2014
Authors: Nilima S. Patil, R. B. Wagh
e6a03ee5-7148-4514-b209-1b4bd07579fd

Nilima S. Patil, R. B. Wagh . A Survey of the Prevalence and Different Techniques for Glaucomatous Image Classification. National Conference on Emerging Trends in Information Technology. NCETIT, 1 (December 2014), 11-13.

@article{
author = { Nilima S. Patil, R. B. Wagh },
title = { A Survey of the Prevalence and Different Techniques for Glaucomatous Image Classification },
journal = { National Conference on Emerging Trends in Information Technology },
issue_date = { December 2014 },
volume = { NCETIT },
number = { 1 },
month = { December },
year = { 2014 },
issn = 0975-8887,
pages = { 11-13 },
numpages = 3,
url = { /proceedings/ncetit/number1/19068-3015/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Emerging Trends in Information Technology
%A Nilima S. Patil
%A R. B. Wagh
%T A Survey of the Prevalence and Different Techniques for Glaucomatous Image Classification
%J National Conference on Emerging Trends in Information Technology
%@ 0975-8887
%V NCETIT
%N 1
%P 11-13
%D 2014
%I International Journal of Computer Applications
Abstract

The recent advance in glaucoma classification method and improvements in the accuracy of classification. An Automated clinical decision support systems are designed to create effective decision support systems for the identification of disease , it is used to extract structural, contextual, or textural features from retinal images which are use to distinguish between normal and diseased samples. The effectiveness is gauged of the resultant ranked and selected subsets of features using a random forest , support vector machine, sequential minimal optimization, and na¨?ve Bayes classification strategies. This paper presents a detailed review on existing classification approaches that have applied to glaucoma classification.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Glaucoma Data Mining Feature Extraction Image Texture Wavelet Transforms Biomedical Optical Imaging.