Detection of Cytopathic Effects Induced by Influenza, Parainfluenza, and Enterovirus Using Deep Convolution Neural Network

Jen-Jee Chen; Po-Han Lin; Yi-Ying Lin; Kun-Yi Pu; Chu-Feng Wang; Shang-Yi Lin; Tzung-Shi Chen

doi:10.3390/biomedicines10010070

Detection of Cytopathic Effects Induced by Influenza, Parainfluenza, and Enterovirus Using Deep Convolution Neural Network

Biomedicines. 2021 Dec 30;10(1):70. doi: 10.3390/biomedicines10010070.

Authors

Jen-Jee Chen^{1

2}, Po-Han Lin³, Yi-Ying Lin⁴, Kun-Yi Pu³, Chu-Feng Wang⁴, Shang-Yi Lin^{4

5

6}, Tzung-Shi Chen⁷

Affiliations

¹ College of Artificial Intelligence, National Yang Ming Chiao Tung University, Hsinchu City 300093, Taiwan.
² Industry Academia Innovation School, National Yang Ming Chiao Tung University, Hsinchu City 300093, Taiwan.
³ Department of Electrical Engineering, National University of Tainan, Tainan 700301, Taiwan.
⁴ Department of Laboratory Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan.
⁵ Division of Infectious Diseases, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan.
⁶ Graduate Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan.
⁷ Department of Computer Science and Information Engineering, National University of Tainan, Tainan 700301, Taiwan.

Abstract

The isolation of a virus using cell culture to observe its cytopathic effects (CPEs) is the main method for identifying the viruses in clinical specimens. However, the observation of CPEs requires experienced inspectors and excessive time to inspect the cell morphology changes. In this study, we utilized artificial intelligence (AI) to improve the efficiency of virus identification. After some comparisons, we used ResNet-50 as a backbone with single and multi-task learning models to perform deep learning on the CPEs induced by influenza, enterovirus, and parainfluenza. The accuracies of the single and multi-task learning models were 97.78% and 98.25%, respectively. In addition, the multi-task learning model increased the accuracy of the single model from 95.79% to 97.13% when only a few data of the CPEs induced by parainfluenza were provided. We modified both models by inserting a multiplexer and de-multiplexer layer, respectively, to increase the correct rates for known cell lines. In conclusion, we provide a deep learning structure with ResNet-50 and the multi-task learning model and show an excellent performance in identifying virus-induced CPEs.

Keywords: ResNet-50; artificial intelligence; cytopathic effect.