ARCHIVES
Research Article
The Deep Neural Network Approach Model forDetecting Covid-19 from X-Ray Scans of Chest
Abhiram Mannam1
Department of Computing Technologies, SRM University, AP, India.
Published Online: May-June 2022
Pages: 618-622
Cite this article
No DOIReferences
[1]. F. Wu, S. Zhao, B. Yu, Y. M. Chen, W. Wang and Z. G. Song, "A new coronavirus associated with human respiratory disease
in China", Nature, vol. 579, no. 7798, pp. 265-269, Mar. 2020.
[2]. A. Kumar, P. K. Gupta and A. Srivastava, "A review of modern technologies for tackling COVID-19 pandemic", Diabetes
Metabolic Syndrome Clin. Res. Rev., vol. 14, no. 4, pp. 569-573, Jul. 2020.
[3]. .Worldometers, Feb. 2021, [online] Available: https://www.worldometers.info/coronavirus/.
[4]. C. Huang, Y. Wang, X. Li, L. Ren, J. Zhao, Y. Hu, et al., "Clinical features of patients infected with 2019 novel coronavirus
in Wuhan China", Lancet, vol. 395, pp. 497-506, May 2020.
[5]. P. Vetter, D. L. Vu, A. G. L’Huillier, M. Schibler, L. Kaiser and F. Jacquerioz, "Clinical features of COVID-19", BMJ, vol. 4, Apr.
2020.
[6]. T. Ai, Z. Yang, H. Hou, C. Zhan, C. Chen, W. Lv, et al., "Correlation of chest CT and RT-PCR testing for coronavirus disease
2019 (COVID-19) in China: A report of 1014 cases", Radiology, vol. 296, Feb. 2020.
[7]. X. Xie, Z. Zhong, W. Zhao, C. Zheng, F. Wang and J. Liu, "Chest CT for typical coronavirus disease 2019 (COVID-19)
pneumonia: Relationship to negative RT-PCR testing", Radiology, vol. 296, no. 2, pp. E41-E45, Aug. 2020.
[8]. M. Barstugan, U. Ozkaya, and S. Ozturk, “Coronavirus (COVID-19) Classification using CT Images by Machine
Learning Methods,” 2020.[9]. P. Kumar, S. Kumari, “Detection of coronavirus Disease (COVID-19) based on Deep
Features”, 2020.
[10]. ApostolopoulosI.D., Mpesiana T.A. Covid-19: automatic detection from X-ray images utilizing transferlearning with convolutional
neural networks.Phys. Eng. Sci. Med. 2020:1–6.
[11]. Z. M. Zain and N. M. Alturki, “COVID-19 pandemic forecasting using CNN-LSTM: a hybrid approach,” Journal of Control
Science and Engineering, vol. 2021, 23 pages, 2021.
[12]. Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, et al. Using artificial intelligence to detect COVID-19 and community-acquired
pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy. Radiology. (2020) 296:E65–71. doi:
10.1148/radiol.2020200905.
in China", Nature, vol. 579, no. 7798, pp. 265-269, Mar. 2020.
[2]. A. Kumar, P. K. Gupta and A. Srivastava, "A review of modern technologies for tackling COVID-19 pandemic", Diabetes
Metabolic Syndrome Clin. Res. Rev., vol. 14, no. 4, pp. 569-573, Jul. 2020.
[3]. .Worldometers, Feb. 2021, [online] Available: https://www.worldometers.info/coronavirus/.
[4]. C. Huang, Y. Wang, X. Li, L. Ren, J. Zhao, Y. Hu, et al., "Clinical features of patients infected with 2019 novel coronavirus
in Wuhan China", Lancet, vol. 395, pp. 497-506, May 2020.
[5]. P. Vetter, D. L. Vu, A. G. L’Huillier, M. Schibler, L. Kaiser and F. Jacquerioz, "Clinical features of COVID-19", BMJ, vol. 4, Apr.
2020.
[6]. T. Ai, Z. Yang, H. Hou, C. Zhan, C. Chen, W. Lv, et al., "Correlation of chest CT and RT-PCR testing for coronavirus disease
2019 (COVID-19) in China: A report of 1014 cases", Radiology, vol. 296, Feb. 2020.
[7]. X. Xie, Z. Zhong, W. Zhao, C. Zheng, F. Wang and J. Liu, "Chest CT for typical coronavirus disease 2019 (COVID-19)
pneumonia: Relationship to negative RT-PCR testing", Radiology, vol. 296, no. 2, pp. E41-E45, Aug. 2020.
[8]. M. Barstugan, U. Ozkaya, and S. Ozturk, “Coronavirus (COVID-19) Classification using CT Images by Machine
Learning Methods,” 2020.[9]. P. Kumar, S. Kumari, “Detection of coronavirus Disease (COVID-19) based on Deep
Features”, 2020.
[10]. ApostolopoulosI.D., Mpesiana T.A. Covid-19: automatic detection from X-ray images utilizing transferlearning with convolutional
neural networks.Phys. Eng. Sci. Med. 2020:1–6.
[11]. Z. M. Zain and N. M. Alturki, “COVID-19 pandemic forecasting using CNN-LSTM: a hybrid approach,” Journal of Control
Science and Engineering, vol. 2021, 23 pages, 2021.
[12]. Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, et al. Using artificial intelligence to detect COVID-19 and community-acquired
pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy. Radiology. (2020) 296:E65–71. doi:
10.1148/radiol.2020200905.
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