Abstract
COVID-19 is a positive-sense single-stranded RNA virus caused by a strain of coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Several noteworthy variants of SARS-CoV-2 were declared by WHO as Alpha, Beta, Gamma, Delta, and Omicron. Till 13/Dec/2022, it has caused 6.65 million death tolls, and over 649 million confirmed positive cases. Based on the convolutional neural network (CNN), this study first proposes a ten-layer CNN as the backbone model. Then, the exponential linear unit (ELU) is introduced to replace ReLU, and the traditional convolutional block is now transformed into conv-ELU. Finally, an ELU-based CNN (ELUCNN) model is proposed for COVID-19 diagnosis. Besides, the MDA strategy is used to enhance the size of the training set. We develop a mobile app integrating ELUCNN, and this web app is run on a client–server modeled structure. Ten runs of the tenfold cross-validation experiment show our model yields a sensitivity of
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\begin{document}$$94.41\pm 0.98$$\end{document}
94.41
±
0.98
, a specificity of
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\begin{document}$$94.84\pm 1.21$$\end{document}
94.84
±
1.21
, an accuracy of
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\begin{document}$$94.62\pm 0.96$$\end{document}
94.62
±
0.96
, and an F1 score of
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\begin{document}$$94.61\pm 0.95$$\end{document}
94.61
±
0.95
. The ELUCNN model and mobile app are effective in COVID-19 diagnosis and give better results than 14 state-of-the-art COVID-19 diagnosis models concerning accuracy.