PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an explainable diagnosis of COVID-19 with multiple-way data augmentation
Article
Wang, S., Zhang, Y., Cheng, X., Zhang, X. and Zhang, Y. 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, pp. 1-18. https://doi.org/10.1155/2021/6633755
| Type | Article |
|---|---|
| Title | PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an explainable diagnosis of COVID-19 with multiple-way data augmentation |
| Authors | Wang, S., Zhang, Y., Cheng, X., Zhang, X. and Zhang, Y. |
| 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. |
| Keywords | General Biochemistry, Genetics and Molecular Biology, Modelling and Simulation, General Immunology and Microbiology, Applied Mathematics, General Medicine |
| Publisher | Hindawi |
| Journal | Computational and Mathematical Methods in Medicine |
| ISSN | 1748-670X |
| Electronic | 1748-6718 |
| Publication dates | |
| 08 Mar 2021 | |
| Online | 10 Mar 2021 |
| Publication process dates | |
| Deposited | 08 Apr 2021 |
| Submitted | 09 Nov 2020 |
| Accepted | 18 Feb 2021 |
| Output status | Published |
| Publisher's version | License |
| Copyright Statement | 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 |
| Digital Object Identifier (DOI) | https://doi.org/10.1155/2021/6633755 |
| Language | English |
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