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SVM and Neural Network based Diagnosis of Diabetic Retinopathy
by
R.priya,
P. Aruna
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 41 - Number 1 |
| Year of Publication: 2012 |
| Authors: R.priya, P. Aruna |
10.5120/5503-7503
|
R.priya, P. Aruna . SVM and Neural Network based Diagnosis of Diabetic Retinopathy. International Journal of Computer Applications. 41, 1 ( March 2012), 6-12. DOI=10.5120/5503-7503
@article{
10.5120/5503-7503,
author = {
R.priya,
P. Aruna
},
title = { SVM and Neural Network based Diagnosis of Diabetic Retinopathy },
journal = {
International Journal of Computer Applications
},
issue_date = { March 2012 },
volume = { 41 },
number = { 1 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = {
6-12
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume41/number1/5503-7503/
},
doi = { 10.5120/5503-7503 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:28:27.670931+05:30
%A R.priya
%A P. Aruna
%T SVM and Neural Network based Diagnosis of Diabetic Retinopathy
%J International Journal of Computer Applications
%@ 0975-8887
%V 41
%N 1
%P 6-12
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Diabetic retinopathy (DR) is an eye disease caused by the complication of diabetes and we should detect it early for effective treatment. As diabetes progresses, the vision of a patient may start deteriorate and lead to diabetic retinopathy. As a result, two groups were identified, namely non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR). In this paper, to diagnose diabetic retinopathy, two models like Probabilistic Neural network (PNN) and Support vector machine (SVM) are described and their performances are compared. Experimental results show that PNN has an accuracy of 89. 60% and SVM has an accuracy of 97. 608 %. This infers that the SVM model outperforms the other model.
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Index Terms
Computer Science
Information Sciences
Keywords