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.

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