Title of article :
PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation
Author/Authors :
Wang, Shui-Hua School of Computer Science - Henan Polytechnic University - China - Henan, China , Zhang, Yin School of Information and Communication Engineering - University of Electronic Science and Technology of China - Chengdu, China , Cheng, Xiaochun School of Science & Technology - Middlesex University - London, UK , Zhang, Xin Department of Medical Imaging - The Fourth People’s Hospital of Huai’an - Huai’an - Jiangsu Province, China , Zhang, Yu-Dong School of Informatics - University of Leicester - Leicester, UK
Abstract :
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 :
COVID-19 , Multiple-Way , Stochastic , PSSPNN
Journal title :
Computational and Mathematical Methods in Medicine
