Deep learning based on model migration for COVID-19 identification

Research Article
Open access

Deep learning based on model migration for COVID-19 identification

Muyang Li 1*
  • 1 Jiangning High School Affiliated To Nanjing Normal University, Jiangsu, China    
  • *corresponding author 1804811673@qq.com
ACE Vol.4
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-55-3
ISBN (Online): 978-1-915371-56-0

Abstract

Since the end of 2019, the virus has gradually spread and eventually spread globally. In this context, it is important to control the spread of COVID-19 quickly. This project attempts to use artificial intelligence to identify CT images of the lungs of COVID-19 patients and facilitate rapid screening of COVID-19 patients. The main focus of this study is to use artificial intelligence based on model transfer deep learning to identify whether patients are infected with novel coronavirus through patient lung images. The difficulty of this task is that the number of lung images of COVID-19 patients is very limited, which makes it very difficult to train traditional neural networks. Traditional computer vision deep learning to extract image features requires a large number of sample data for model training. If the number of images in the data set is too small, the model will overfit and fail to achieve relatively accurate COVID-19 identification effect. To solve the above problems, this paper studied novel coronavirus identification of patients' lung CT images by deep learning method based on model transfer. We build models based on similar types of problems, store those models and then fine-tune them. Eventually, a model was trained to recognize images of the lungs of COVID-19 patients. The method was tested on publicly available COVID-19 datasets, and the results showed that the identification accuracy of the method was about 70%.

Keywords:

Covid-19, Deep Learning, Identify, Model migration.

Li,M. (2023). Deep learning based on model migration for COVID-19 identification. Applied and Computational Engineering,4,111-118.
Export citation

References

[1]. Dolbneva D V . The Impact of COVID-19 on the World's Economies[J]. THE PROBLEMS OF ECONOMY, 2020, 1(43):20-26.

[2]. Chengying, Wen Yuechun, Chang Yali, et al. Analysis on the impact of novel coronavirus pneumonia epidemic on Chinese economy [J]. Journal of Quantitative and Technical Economics, 2020, 37(5):20.

[3]. Jiang Rong, Xie Huarong, Duan Yongsheng. Prevention and control strategies of nosocomial infection in COVID-19 medical institutions [J]. Chinese Contemporary Medicine, 2020, 27(30):3.

[4]. Hu Xiaobo, Zhang Peng, An Ji, et al. Research on automatic medical image recognition technology based on computer vision [J]. Microcomputer Information, 2012(10):2.

[5]. Shi Xiangbin, Fang Xuejian, Zhang Deyuan, et al. Image classification based on Deep Learning Hybrid model transfer learning [J]. Journal of System Simulation, 2016, 28(1):8.

[6]. Zhang Zhenhua, Jixiang, Zhang Jinsong, et al. Analysis of CT image characteristics of COVID-19 based on AI technology [J]. Medical Equipment, 2020, 41(5):4.

[7]. S M Humphries, A M Notartary, J P Caceno, et al. Deep learning can realize automatic classification of emphysema on CT [J]. International Journal of Medical Radiology, 2020, V. 43(02):120-120.

[8]. Hui Rui, Gao Xiaohong, Tian Zengmin. CT brain image classification method based on deep learning for preliminary screening of Alzheimer's disease [J]. China Medical Equipment, 2017, 32(12):5.

[9]. Zhang Weiqi, Jiang Yufei, Yuan Huiyun. Research on privacy protection of COVID-19 patients [J]. Chinese Medical Ethics, 2021, 34(10):5.

[10]. [10]LI H. Analysis of overfitting phenomenon based on deep learning [J]. China Science and Technology Information, 2020(14):2. (in Chinese)

[11]. Chen Yufeng, Chen Jianwen, Hou Jiayi. Radiological image recognition of viral pneumonia based on deep learning framework KERAS [J]. Electronic Components and Information Technology, 2021.

[12]. Rajpurkar P , Irvin J , Zhu K , et al. CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning[J]. 2017.

[13]. Yuan Jianyong, Yu Yuanming, Wang Chao. A Training model saving method and driver based on Tensorflow, Computing server: ,CN108446173A[P].2018.

Cite this article

Li,M. (2023). Deep learning based on model migration for COVID-19 identification. Applied and Computational Engineering,4,111-118.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-55-3(Print) / 978-1-915371-56-0(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.4
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. Dolbneva D V . The Impact of COVID-19 on the World's Economies[J]. THE PROBLEMS OF ECONOMY, 2020, 1(43):20-26.

[2]. Chengying, Wen Yuechun, Chang Yali, et al. Analysis on the impact of novel coronavirus pneumonia epidemic on Chinese economy [J]. Journal of Quantitative and Technical Economics, 2020, 37(5):20.

[3]. Jiang Rong, Xie Huarong, Duan Yongsheng. Prevention and control strategies of nosocomial infection in COVID-19 medical institutions [J]. Chinese Contemporary Medicine, 2020, 27(30):3.

[4]. Hu Xiaobo, Zhang Peng, An Ji, et al. Research on automatic medical image recognition technology based on computer vision [J]. Microcomputer Information, 2012(10):2.

[5]. Shi Xiangbin, Fang Xuejian, Zhang Deyuan, et al. Image classification based on Deep Learning Hybrid model transfer learning [J]. Journal of System Simulation, 2016, 28(1):8.

[6]. Zhang Zhenhua, Jixiang, Zhang Jinsong, et al. Analysis of CT image characteristics of COVID-19 based on AI technology [J]. Medical Equipment, 2020, 41(5):4.

[7]. S M Humphries, A M Notartary, J P Caceno, et al. Deep learning can realize automatic classification of emphysema on CT [J]. International Journal of Medical Radiology, 2020, V. 43(02):120-120.

[8]. Hui Rui, Gao Xiaohong, Tian Zengmin. CT brain image classification method based on deep learning for preliminary screening of Alzheimer's disease [J]. China Medical Equipment, 2017, 32(12):5.

[9]. Zhang Weiqi, Jiang Yufei, Yuan Huiyun. Research on privacy protection of COVID-19 patients [J]. Chinese Medical Ethics, 2021, 34(10):5.

[10]. [10]LI H. Analysis of overfitting phenomenon based on deep learning [J]. China Science and Technology Information, 2020(14):2. (in Chinese)

[11]. Chen Yufeng, Chen Jianwen, Hou Jiayi. Radiological image recognition of viral pneumonia based on deep learning framework KERAS [J]. Electronic Components and Information Technology, 2021.

[12]. Rajpurkar P , Irvin J , Zhu K , et al. CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning[J]. 2017.

[13]. Yuan Jianyong, Yu Yuanming, Wang Chao. A Training model saving method and driver based on Tensorflow, Computing server: ,CN108446173A[P].2018.

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.