Research on Recognition Method of COVID-19 Images Based on Deep Learning

Dongsheng Ji; Yanzhong Zhao; Zhujun Zhang; Qianchuan Zhao

doi:10.1051/e3sconf/202127101039

All issues

Volume 271 (2021)

E3S Web Conf., 271 (2021) 01039

References

Open Access

Issue		E3S Web Conf. Volume 271, 2021 2021 2^nd International Academic Conference on Energy Conservation, Environmental Protection and Energy Science (ICEPE 2021)


Article Number		01039
Number of page(s)		5
Section		Energy Development and Utilization and Energy Storage Technology Application
DOI		https://doi.org/10.1051/e3sconf/202127101039
Published online		15 June 2021

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[1] Novel Coronavirus Pneumonia Tracking[EB/OL]. http://www.bing.com/covid. (2020) [Google Scholar]

[2] National Health Commission of the People's Republie of China [EB/OL]. http://www.nhc.gov.cn. (2020) [Google Scholar]

[3] National Administration of Traditional Chinese Medicine [EB/OL]. http://www.satcm.gov.cn. (2020) [Google Scholar]

[4] W. Guan, Z. Ni, Y. Hu, W. Liang, C. Ou, J. He, L. Liu, H. Shan, C. Lei, and D. Hui, “Clinical characteristics of coronavirus disease 2019 in China,” New England journal of medicine, 382, 1708–1720 (2020) [CrossRef] [Google Scholar]

[5] F. Wu, S. Zhao, B. Yu, Y. Chen, W. Wang, Z. Song, Y. Hu, Z. Tao, J. Tian, and Y. Pei, “A new coronavirus associated with human respiratory disease in China,” Nature, 579, 265–269 (2020) [CrossRef] [PubMed] [Google Scholar]

[6] Ying Song, Shuangjia Zheng, Liang Li, Xiang Zhang, et al. Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images. medRxiv. (2020) [Google Scholar]

[7] Z. Feng, Q. Yu, S. Yao, and L. Luo, “Early Prediction of Disease Progression in 2019 Novel Coronavirus Pneumonia Patients Outside Wuhan with CT and Clinical Characteristics,” medRxiv, (2020) [Google Scholar]

[8] F. Shan, Y. Gao, J. Wang, W. Shi, N. Shi, M. Han, Z. Xue, D. Shen, and Y. Shi, “Lung infection quantification of COVID-19 in CT images with deep learning,” arXiv preprint arXiv:2003.04655, (2020) [Google Scholar]

[9] Y. Wang, M. Hu, Q. Li, X. Zhang, G. Zhai, and N. Yao, “Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner,” arXivpreprint arXiv:2002.05534, (2020) [Google Scholar]

[10] L. Yan, H. Zhang, Y. Xiao, M. Wang, Y. Guo, C. Sun, X. Tang, L. Jing, S. Li, and M. Zhang, “Prediction of criticality in patients with severe Covid-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan,” MedRxiv, (2020) [Google Scholar]

[11] S. Wang, B. Kang, J. Ma, and X. Zeng, “A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19),” medRxiv, (2020) [Google Scholar]

[12] C. Zheng, X. Deng, and Q. Fu, “Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label,” medRxiv, (2020) [Google Scholar]

[13] A. Abbas, M. Abdelsamea, and M. Gaber, “Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network,” medRxiv, (2020) [Google Scholar]