PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an explainable diagnosis of COVID-19 with multiple-way data augmentation

Wang, Shui-Hua ORCID: https://orcid.org/0000-0003-4713-2791, Zhang, Yin, Cheng, Xiaochun ORCID: https://orcid.org/0000-0003-0371-9646, Zhang, Xin ORCID: https://orcid.org/0000-0001-9769-2856 and Zhang, Yu-Dong ORCID: https://orcid.org/0000-0002-4870-1493 (2021) PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an explainable diagnosis of COVID-19 with multiple-way data augmentation. Computational and Mathematical Methods in Medicine, 2021 , 6633755. pp. 1-18. ISSN 1748-670X [Article] (doi:10.1155/2021/6633755)

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Abstract

Aim. COVID-19 has caused large death tolls all over the world. Accurate diagnosis is of significant importance for early treatment. Methods. In this study, we proposed a novel PSSPNN model for classification between COVID-19, secondary pulmonary tuberculosis, community-captured pneumonia, and healthy subjects. PSSPNN entails five improvements: we first proposed the n-conv stochastic pooling module. Second, a novel stochastic pooling neural network was proposed. Third, PatchShuffle was introduced as a regularization term. Fourth, an improved multiple-way data augmentation was used. Fifth, Grad-CAM was utilized to interpret our AI model. Results. The 10 runs with random seed on the test set showed our algorithm achieved a microaveraged F1 score of 95.79%. Moreover, our method is better than nine state-of-the-art approaches. Conclusion. This proposed PSSPNN will help assist radiologists to make diagnosis more quickly and accurately on COVID-19 cases.

Item Type: Article
Keywords (uncontrolled): General Biochemistry, Genetics and Molecular Biology, Modelling and Simulation, General Immunology and Microbiology, Applied Mathematics, General Medicine
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 32820
Notes on copyright: Copyright © 2021 Shui-Hua Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
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Depositing User: Jisc Publications Router
Date Deposited: 08 Apr 2021 10:20
Last Modified: 12 Apr 2021 11:27
URI: https://eprints.mdx.ac.uk/id/eprint/32820

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