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.
Highlights
COVID-19 is a type of disease caused by a new strain of coronavirus
We proposed a PatchShuffle SPNN (PSSPNN), which entails five improvements: (i) proposed n-conv stochastic pooling module (NCSPM) module, (ii) usage of stochastic pooling, (iii) usage of PatchShuffle, (iv) improved multiple-way data augmentation, and (v) explainability via Grad-CAM
Those five improvements enable our artificial intelligence (AI) model to deliver improved performances compared to 9 state-ofthe-art approaches
Summary
COVID-19 is a type of disease caused by a new strain of coronavirus. “CO” means corona, “VI” virus, and “D” disease. The additional advantage of CCT is that it can provide highquality, three-dimensional chest data where radiologists can clearly view the COVID-19 lesions, which may be obscure in the other two CI approaches. Jiang and Zhang [8] proposed a 6-level convolutional neural network (6L-CNN) for therapy and rehabilitation Their performances were improved by replacing the traditional rectified linear unit with a leaky rectified linear unit. Wang et al [13] proposed a 3D deep convolutional neural network to detect COVID-19 (DeCovNet). Zhang et al [14] proposed a seven-layer convolutional neural network for COVID-19 diagnosis (7L-CCD). Their performance achieved an accuracy of 94:03 ± 0:80 for the binary classification task (COVID-19 against healthy subjects). (iii) A more advanced neural network, PatchShuffle SPNN (PSSPNN), is proposed where PatchShuffle is introduced as the regularization term in the loss function of SPNN (iv) An improved multiple-way data augmentation is utilized to help the network avoid overfitting (v) Grad-CAM is used to show the explainable heatmap, which displays association with lung lesions
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