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Automatic Classification of Cataract based on Deep Learnig
by
Jingchao Sun,
Lu Liu
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 177 - Number 27 |
| Year of Publication: 2019 |
| Authors: Jingchao Sun, Lu Liu |
10.5120/ijca2019919687
|
Jingchao Sun, Lu Liu . Automatic Classification of Cataract based on Deep Learnig. International Journal of Computer Applications. 177, 27 ( Dec 2019), 1-5. DOI=10.5120/ijca2019919687
@article{
10.5120/ijca2019919687,
author = {
Jingchao Sun,
Lu Liu
},
title = { Automatic Classification of Cataract based on Deep Learnig },
journal = {
International Journal of Computer Applications
},
issue_date = { Dec 2019 },
volume = { 177 },
number = { 27 },
month = { Dec },
year = { 2019 },
issn = { 0975-8887 },
pages = {
1-5
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume177/number27/31066-2019919687/
},
doi = { 10.5120/ijca2019919687 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:47:02.322078+05:30
%A Jingchao Sun
%A Lu Liu
%T Automatic Classification of Cataract based on Deep Learnig
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 27
%P 1-5
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Cataract is a serious eye disease which may cause blindness. Early detection is of high significance to the treatment of cataract, which reduces the risk of patients to turning into blindness. Some studies were conducted for fundus image classification based on traditional machine learning methods. However, their performance still can be improved. Therefore, a novel deep learning based method is proposed to classify cataract using fundus images. To avoid relying on mass labelled data, the proposed method resorts to deep transfer learning to reduce the number of parameters that need to be trained. Consequently, in the proposed method, the first five convolutional layers of AlexNet are utilized for general feature learning; then three subsequent layers are designed and fine-tuned for the classification of fundus images. The best performance for cataract detection is 95.37% in terms of classification accuracy.
References
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