Using Deep Neural Networks to Classify the Severity of Diabetic Retinopathy

Chun Ying Chen; Meng Chou Chang

doi:10.1109/ICCE-Taiwan55306.2022.9869142

Using Deep Neural Networks to Classify the Severity of Diabetic Retinopathy

Chun Ying Chen, Meng Chou Chang

National Changhua University of Education

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

Diabetic Retinopathy (DR) is a type of retinopathy resulted from diabetic hyperglycemia (high blood glucose), which causes the abnormal blood vessels of eyes to bleed and causes the patients to lose sight. In this paper, we employ two deep neural networks, InceptionV3 and EfficientNet, to classify the retinal fundus images of DR patients into five severity levels of DR, and improve the accuracy of the neural networks by techniques such as dropout, data preprocessing, data augmentation, and learning rate adjustment. The experimental results showed that EfficientNetB0 has the best performance with an accuracy of 86.26% and a quadratic weighted kappa of 0.926.

Original language	English
Title of host publication	Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	241-242
Number of pages	2
ISBN (Electronic)	9781665470506
DOIs	https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869142
State	Published - 2022
Externally published	Yes
Event	2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022 - Taipei, Taiwan Duration: 06 07 2022 → 08 07 2022

Publication series

Name	Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

Conference

Conference	2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
Country/Territory	Taiwan
City	Taipei
Period	06/07/22 → 08/07/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Deep neural network
deep learning
diabetic retinopathy

Access to Document

10.1109/ICCE-Taiwan55306.2022.9869142

Cite this

Chen, C. Y., & Chang, M. C. (2022). Using Deep Neural Networks to Classify the Severity of Diabetic Retinopathy. In Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022 (pp. 241-242). (Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869142

Chen, Chun Ying ; Chang, Meng Chou. / Using Deep Neural Networks to Classify the Severity of Diabetic Retinopathy. Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 241-242 (Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022).

@inproceedings{2f46f66cd5d7436a88fa0aa5deb06291,

title = "Using Deep Neural Networks to Classify the Severity of Diabetic Retinopathy",

abstract = "Diabetic Retinopathy (DR) is a type of retinopathy resulted from diabetic hyperglycemia (high blood glucose), which causes the abnormal blood vessels of eyes to bleed and causes the patients to lose sight. In this paper, we employ two deep neural networks, InceptionV3 and EfficientNet, to classify the retinal fundus images of DR patients into five severity levels of DR, and improve the accuracy of the neural networks by techniques such as dropout, data preprocessing, data augmentation, and learning rate adjustment. The experimental results showed that EfficientNetB0 has the best performance with an accuracy of 86.26\% and a quadratic weighted kappa of 0.926.",

keywords = "Deep neural network, deep learning, diabetic retinopathy",

author = "Chen, \{Chun Ying\} and Chang, \{Meng Chou\}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022 ; Conference date: 06-07-2022 Through 08-07-2022",

year = "2022",

doi = "10.1109/ICCE-Taiwan55306.2022.9869142",

language = "英语",

series = "Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "241--242",

booktitle = "Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022",

address = "美国",

}

Chen, CY & Chang, MC 2022, Using Deep Neural Networks to Classify the Severity of Diabetic Retinopathy. in Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022. Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022, Institute of Electrical and Electronics Engineers Inc., pp. 241-242, 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022, Taipei, Taiwan, 06/07/22. https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869142

Using Deep Neural Networks to Classify the Severity of Diabetic Retinopathy. / Chen, Chun Ying; Chang, Meng Chou.
Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 241-242 (Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Using Deep Neural Networks to Classify the Severity of Diabetic Retinopathy

AU - Chen, Chun Ying

AU - Chang, Meng Chou

PY - 2022

Y1 - 2022

N2 - Diabetic Retinopathy (DR) is a type of retinopathy resulted from diabetic hyperglycemia (high blood glucose), which causes the abnormal blood vessels of eyes to bleed and causes the patients to lose sight. In this paper, we employ two deep neural networks, InceptionV3 and EfficientNet, to classify the retinal fundus images of DR patients into five severity levels of DR, and improve the accuracy of the neural networks by techniques such as dropout, data preprocessing, data augmentation, and learning rate adjustment. The experimental results showed that EfficientNetB0 has the best performance with an accuracy of 86.26% and a quadratic weighted kappa of 0.926.

AB - Diabetic Retinopathy (DR) is a type of retinopathy resulted from diabetic hyperglycemia (high blood glucose), which causes the abnormal blood vessels of eyes to bleed and causes the patients to lose sight. In this paper, we employ two deep neural networks, InceptionV3 and EfficientNet, to classify the retinal fundus images of DR patients into five severity levels of DR, and improve the accuracy of the neural networks by techniques such as dropout, data preprocessing, data augmentation, and learning rate adjustment. The experimental results showed that EfficientNetB0 has the best performance with an accuracy of 86.26% and a quadratic weighted kappa of 0.926.

KW - Deep neural network

KW - deep learning

KW - diabetic retinopathy

UR - https://www.scopus.com/pages/publications/85138754396

U2 - 10.1109/ICCE-Taiwan55306.2022.9869142

DO - 10.1109/ICCE-Taiwan55306.2022.9869142

M3 - 会议稿件

AN - SCOPUS:85138754396

T3 - Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

SP - 241

EP - 242

BT - Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

Y2 - 6 July 2022 through 8 July 2022

ER -

Chen CY, Chang MC. Using Deep Neural Networks to Classify the Severity of Diabetic Retinopathy. In Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 241-242. (Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022). doi: 10.1109/ICCE-Taiwan55306.2022.9869142

Using Deep Neural Networks to Classify the Severity of Diabetic Retinopathy

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this