CNN-resNet-50 Algorithm to Detect the Novel Coronavirus (COVID-19) Using CT Images
Keywords:
Convolutional neural networks, COVID-19, CT scan images, ResNet-50Abstract
The detection of the Covid-19 pandemic has been crucial in ensuring health safety and preventing the spread of the virus. In this paper, we present a novel approach by combining the CNN and ResNet-50 algorithms into a unified model. Our experimental results showcase the remarkable efficiency of the proposed method, outperforming both the individual CNN and ResNet-50 approaches. The hybrid CNN-ResNet-50 algorithm demonstrates its ability to automatically and effectively assist in the early diagnosis of COVID-19 patients. By leveraging this combined model, we aim to contribute to the improved detection and management of the pandemic.
References
Huang, Chaolin, et al. "Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China." The lancet 395.10223 (2020): 497-506.
Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, Tao Q, Sun Z, Xia L. Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology 2020:200642. https://doi.org/10.1148/ radiol.2020200642.
Jibril SIAML, Islam Md M, Sharif US. Predictive data mining models for novel coronavirus (COVID-19) infected patients recovery. SN Comput Sci 2020;1:206. https://doi.org/10.1007/s42979-020-00216-w.
Li Y, Xia L. Coronavirus disease 2019 (COVID-19): role of chest CT in diagnosis and management. AJR Am J Roentgenol 2020;1–7. https://doi.org/10.2214/ AJR.20.22954.
Fang Y, Zhang H, Xie J, Lin M, Ying L, Pang P, Ji W. Sensitivity of chest CT for COVID-19: comparison to RT-PCR, Radiology. 200432, https://doi.org/10.114 8/radiol.2020200432; 2020.
CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images
Lafraxo, Samira, and Mohamed El Ansari. "CoviNet: automated COVID-19 detection from X-rays using deep learning techniques." 2020 6th IEEE congress on information science and technology (CiSt). IEEE, 2021.
Ying Y, Zheng S, Li S, Zhang L, Zhang X, Huang X, ChenZ, Zhao J, Jie H, Wang Y, Chong R, Shen Y, Zha J, Yang Y. Deep learning enables accurate diagnosis of novel coronavirus(COVID-19) with CT images. medRxiv. 2020.
Cheng Jin JF, Chen W, Cao Y, Zhanwei X, Zhang X, Deng L, Zheng C, Zhou J, Shi H. Development and evaluation of an AI system for COVID-19 diagnosis. medRxiv. 2020.
Butt C, Gill J, Chun D, Babu BA. Deep learning system to screen coronavirus disease pneumonia. Appl Intell. 2019.
Wang L, Wong A. COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images. 2020.
Wu X, Hui H, Niu M, Li L, Wang L, He B, Yang X, Li L, Li H, Tian J, Zha Y. Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: a multicenter study. Eur J Radiol. 2020;128:1–9.
Khan AI, Shah JL, Bhat MM. CoroNet: a deep neural network for detection and diagnosis of COVID-19 from chest X-ray images. Comput Methods Programs Biomed. 2020.
Wang S, Kang B, Ma J, Zeng X, Xiao M, Guo J, Cai M, Yang J,Li Y, Meng X, Xu B. A deep learning algorithm using CT images to screen for corona virus disease (COVID-19). medRxiv. 2020.
Ticona, Javier H., Victoria M. Zaccone, and Isabel M. McFarlane. "Community-acquired pneumonia: A focused review." American journal of medical case reports 9.1 (2021): 45.
Afshar, P., Heidarian, S., Enshaei, N. et al. COVID-CT-MD, COVID-19 computed tomography scan dataset applicable in machine learning and deep learning. Sci Data 8, 121 (2021). https://doi.org/10.1038/s41597-021-00900-3
Victor Ikechukwu, A., Murali, S., Deepu, R., & Shivamurthy, R. (2021). ResNet-50 vs VGG-19 vs training from scratch: A comparative analysis of the segmentation and classification of Pneumonia from chest X-ray images. Global Transitions Proceedings, 2(2), 375–381. https://doi.org/10.1016/j.gltp.2021.08.027
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