[1]
A. C. Walls, Y. J. Park, M. A. Tortorici, A. Wall, A. T. McGuire, and D. Veesler, Structure, function, and antigenicity of the SARS-CoV-2 spike glycoprotein, Cell, vol. 181, no. 2, pp. 281-292, 2020.
[3]
N. S. Chen, M. Zhou, X. Dong, J. M. Qu, F. Y. Gong, Y. Han, Y. Qiu, J. L. Wang, Y. Liu, Y. Wei, et al., Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study, Lancet, vol. 395, no. 10 223, pp. 507-513, 2020.
[4]
Q. Li, X. H. Guan, P. Wu, X. Y. Wang, L. Zhou, Y. Q. Tong, R. Q. Ren, K. S. M. Leung, E. H. Y. Lau, J. Y. Wong, et al., Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia, New England Journal of Medicine, vol. 382, no. 13, pp. 1199-1207, 2020.
[5]
J. F. W. Chan, S. F. Yuan, K. H. Kok, K. K. W. To, H. Chu, J. Yang, F. F. Xing, J. L. Liu, C. C. Y. Yip, R. W. S. Poon, et al., A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: A study of a family cluster, Lancet, vol. 395, no. 10 223, pp. 514-523, 2020.
[6]
D. W. Wang, B. Hu, C. Hu, F. F. Zhu, X. Liu, J. Zhang, B. B. Wang, H. Xiang, Z. S. Cheng, Y. Xiong, et al., Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China, JAMA, vol. 323, no. 11, pp. 1061-1069, 2020.
[7]
C. L. Huang, Y. M. Wang, X. W. Li, L. L. Ren, J. P. Zhao, Y. Hu, L. Zhang, G. H. Fan, J. Y. Xu, X. Y. Gu, et al., Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China, Lancet, vol. 395, no. 10 223, pp. 497-506, 2020.
[8]
X. Y. Ou, Y. Liu, X. B. Lei, P. Li, D. Mi, L. L. Ren, L. Guo, R. X. Guo, T. Chen, J. X. Hu, et al., Characterization of spike glycoprotein of SARS-CoV-2 on virus entry and its immune cross-reactivity with SARS-CoV, Nature Communications, vol. 11, p. 1620, 2020.
[9]
P. Zhai, Y. B. Ding, X. Wu, J. K. Long, Y. J. Zhong, and Y. M. Li, The epidemiology, diagnosis and treatment of COVID-19, International Journal of Antimicrobial Agents, vol. 55, no. 5, p. 105 955, 2020.
[10]
X. Z. Xie, Z. Zhong, W. Zhao, C. Zheng, F. Wang, and J. Liu, Chest CT for typical 2019-nCoV pneumonia: Relationship to negative RT-PCR testing, Radiology, vol. 296, no. 2, p. 200 343, 2020.
[11]
D. S. Kermany, M. Goldbaum, W. J. Cai, C. C. S. Valentim, H. Y. Liang, S. L. Baxter, A. McKeown, G. Yang, X. K. Wu, F. B. Yan, et al., Identifying medical diagnoses and treatable diseases by image-based deep learning, Cell, vol. 172, no. 5, pp. 1122-1131, 2018.
[12]
S. Wang, B. Kang, J. L. Ma, X. J. Zeng, M. M. Xiao, J. Guo, M. J. Cai, J. Y. Yang, Y. D. Li, X. F. Meng, et al., A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19), , 2020.
[13]
X. W. Xu, X. G. Jiang, C. L. Ma, P. Du, X. K. Li, S. Z. Lv, L. Yu, Y. F. Chen, J. W. Su, G. J. Lang, et al., Deep learning system to screen coronavirus disease 2019 pneumonia, arXiv preprint arXiv: 2002.09334, 2020.
[14]
J. P. Cohen, P. Morrison, and L. Dao, COVID-19 image data collection, arXiv preprint arXiv: 2003.11597, 2020.
[15]
O. Gozes, M. Frid-Adar, H. Greenspan, P. D. Browning, H. Q. Zhang, W. B. Ji, A. Bernheim, and E. Siegel, Rapid AI development cycle for the coronavirus (COVID-19) pandemic: Initial results for automated detection & patient monitoring using deep learning CT image analysis, arXiv preprint arXiv: 2003.05037, 2020.
[16]
D. Varshni, K. Thakral, L. Agarwal, R. Nijhawan, and A. Mittal, Pneumonia detection using CNN based feature extraction, in Proc. 2019 IEEE Int. Conf. Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India, 2019, pp. 1-7.
[17]
A. Mehrotra and K. K. Singh, Detection of 2011 Tohoku tsunami induced changes in Rikuzentakata using normalized wavelet fusion and probabilistic neural network, Disaster Advances, vol. 7, no. 2, pp. 1-8, 2014.
[18]
F. Chollet, Xception: Deep learning with depthwise separable convolutions, in Proc. 2017 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 1800-1807.
[19]
K. K. Singh, M. Siddhartha, and A. Singh, Diagnosis of Coronavirus Disease (COVID-19) from Chest X-Ray images using modified XceptionNet, Romanian Journal of Information Science and Technology, vol. 23, no. S, pp. S91-S115, 2020.
[20]
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting, The Journal of Machine Learning Research, vol. 15, no. 1, pp. 1929-1958, 2014.
[21]
A. F. Agarap, Deep learning using rectified linear units (ReLU), arXiv preprint arXiv: 1803.08375, 2018.
[22]
A. Mikolajczyk and M. Grochowski, Data augmentation for improving deep learning in image classification problem, presented at 2018 Int. Interdisciplinary PhD Workshop (IIPhDW), Swinoujście, Poland, 2018, pp. 117-122.
[23]
J. P. Cohen, P. Morrison, and L. Dao, COVID-19 image data collection, arXiv preprint arXiv: 2003.11597, 2020.
[25]
A. Ben-David, Comparison of classification accuracy using Cohen’s Weighted Kappa, Expert Systems with Applications, vol. 34, no. 2, pp. 825-832, 2008.
[26]
A. Abbas, M. M. Abdelsamea, and M. M. Gaber, Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network, arXiv preprint arXiv: 2003.13815, 2020.
[27]
E. Luz, P. L. Silva, R. Silva, L. Silva, G. Moreira, and D. Menotti, Towards an effective and efficient deep learning model for COVID-19 patterns detection in X-ray images, arXiv preprint arXiv: 2004.05717, 2020.