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Identifying Abnormalities in the Retinal Images using SVM Classifiers

by Shantala Giraddi, Jagadeesh Pujari, Shivanand Seeri
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 111 - Number 6
Year of Publication: 2015
Authors: Shantala Giraddi, Jagadeesh Pujari, Shivanand Seeri
10.5120/19540-9686

Shantala Giraddi, Jagadeesh Pujari, Shivanand Seeri . Identifying Abnormalities in the Retinal Images using SVM Classifiers. International Journal of Computer Applications. 111, 6 ( February 2015), 5-8. DOI=10.5120/19540-9686

@article{ 10.5120/19540-9686,
author = { Shantala Giraddi, Jagadeesh Pujari, Shivanand Seeri },
title = { Identifying Abnormalities in the Retinal Images using SVM Classifiers },
journal = { International Journal of Computer Applications },
issue_date = { February 2015 },
volume = { 111 },
number = { 6 },
month = { February },
year = { 2015 },
issn = { 0975-8887 },
pages = { 5-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume111/number6/19540-9686/ },
doi = { 10.5120/19540-9686 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:47:07.880459+05:30
%A Shantala Giraddi
%A Jagadeesh Pujari
%A Shivanand Seeri
%T Identifying Abnormalities in the Retinal Images using SVM Classifiers
%J International Journal of Computer Applications
%@ 0975-8887
%V 111
%N 6
%P 5-8
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Automated early detection of exudates in retinal images is a challenging task. With the global diabetic population increasing at an alarming rate, there is need for development of automated systems for detection of exudates. The main obstacle in exudates detection is extreme variability of color and contrast in retinal images that depends on the degree of pigmentation, size of the pupil and illumination. The aim of this paper is to develop and validate systems for detection of hard exudates and classify the input image as normal or diseased one. The authors have proposed and implemented novel method based on color and texture features. Performance analysis of SVM and KNN classifiers is presented. Images classified by these classifiers are validated by expert opthamalagists.

References
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  5. Shantala Giraddi, Dr Jagadeesh Pujari, "Role of GLCM features in Identifying Abnormalities in the Retinal Images", Accepted IJGSP To be published
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Index Terms

Computer Science
Information Sciences

Keywords

Diabetic Retinopathy SVM classifier KNN classifier Exudates.

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