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

研究成果: 圖書/報告稿件的類型 › 會議稿件 › 同行評審

5 引文斯高帕斯（Scopus）

摘要

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.

原文	英語
主出版物標題	Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
發行者	Institute of Electrical and Electronics Engineers Inc.
頁面	241-242
頁數	2
ISBN（電子）	9781665470506
DOIs	https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869142
出版狀態	已出版 - 2022
對外發佈	是
事件	2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022 - Taipei, 台灣持續時間: 06 07 2022 → 08 07 2022

出版系列

名字	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
國家/地區	台灣
城市	Taipei
期間	06/07/22 → 08/07/22

文獻附註

Publisher Copyright:
© 2022 IEEE.

UN SDG

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10.1109/ICCE-Taiwan55306.2022.9869142

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Link to publication in Scopus

引用此

Chen, C. Y., & Chang, M. C. (2022). Using Deep Neural Networks to Classify the Severity of Diabetic Retinopathy. 於 Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022 (頁 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. 頁 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. 於 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., 頁 241-242, 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022, Taipei, 台灣, 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).

研究成果: 圖書/報告稿件的類型 › 會議稿件 › 同行評審

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 - http://www.scopus.com/inward/record.url?scp=85138754396&partnerID=8YFLogxK

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. 於 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

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