Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023)

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A COVID-19 Detection Method based on Deep Learning Model Trained by Chest X-Ray Images

Authors
Guanhua Li1, Zhenzhu Yin2, *
1School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, 300384, China
2School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, 300384, China
*Corresponding author. Email: vivi@stud.tjut.edu.cn
Corresponding Author
Zhenzhu Yin
Available Online 14 February 2024.
DOI
10.2991/978-94-6463-370-2_36How to use a DOI?
Keywords
Image recognition; CXR Image; CNN; ResNet152; COVID-19
Abstract

COVID-19 is a contagious disease, with tens of millions of people worldwide infected. Taking Chest X-Ray (CXR) images is an important step during clinical diagnosis, because doctors could monitor the lung status directly by this way. In this paper, we deploy three models: pretrained ResNet152, Linear discriminant analysis (LDA), and Support Vector Machine (SVM). We train these models with different sample sizes: 100, 1500, and 3307, to observe their performances, and ResNet152 all outperforms the other two method. A 96.06% accuracy, a 96.12% precision, a 96.06% recall, and a 96.06% F1 Score are attained (the indicators are averaged weighted), demonstrating that ResNet152 has great strength and potential in CXR recognition field. Besides, we discuss the reason for the underperform of SVM and LDA. Furthermore, 10 independent repeated tests verified the prominent stability of ResNet152. The four indicators’ extreme deviations obtained are all within 1.23%. This work indicate that ResNet152 is a very effective model, and can greatly assist the healthcare industry in the future.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023)
Series
Advances in Intelligent Systems Research
Publication Date
14 February 2024
ISBN
10.2991/978-94-6463-370-2_36
ISSN
1951-6851
DOI
10.2991/978-94-6463-370-2_36How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Guanhua Li
AU  - Zhenzhu Yin
PY  - 2024
DA  - 2024/02/14
TI  - A COVID-19 Detection Method based on Deep Learning Model Trained by Chest X-Ray Images
BT  - Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023)
PB  - Atlantis Press
SP  - 334
EP  - 342
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-370-2_36
DO  - 10.2991/978-94-6463-370-2_36
ID  - Li2024
ER  -