PSSPNN: PatchShuffle stochastic pooling neural network for an explainable diagnosis of COVID-19 with multiple-way data augmentation

COVID-19 nowadays caused numerous 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 micro-averaged 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.