Deep Rank-Based Average Pooling Network for Covid-19 Recognition |
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Authors: | Shui-Hua Wang Muhammad Attique Khan Vishnuvarthanan Govindaraj Steven L. Fernandes Ziquan Zhu Yu-Dong Zhang |
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Affiliation: | 1.Department of Electrical Engineering, University of Engineering and Technology Peshawar, Pakistan2 Center for Artificial Intelligence Technology, Faculty of Information Science & Technology, Universiti Kebangsaan, Kajang, 43000, Malaysia3 Department of Computer Science and Engineering, Chungnam National University, Daejeon, 34134, Korea |
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Abstract: | (Aim) To make a more accurate and precise COVID-19 diagnosis system, this study proposed a novel deep rank-based average pooling network (DRAPNet) model, i.e., deep rank-based average pooling network, for COVID-19 recognition. (Methods) 521 subjects yield 1164 slice images via the slice level selection method. All the 1164 slice images comprise four categories: COVID-19 positive; community-acquired pneumonia; second pulmonary tuberculosis; and healthy control. Our method firstly introduced an improved multiple-way data augmentation. Secondly, an n-conv rank-based average pooling module (NRAPM) was proposed in which rank-based pooling—particularly, rank-based average pooling (RAP)—was employed to avoid overfitting. Third, a novel DRAPNet was proposed based on NRAPM and inspired by the VGG network. Grad-CAM was used to generate heatmaps and gave our AI model an explainable analysis. (Results) Our DRAPNet achieved a micro-averaged F1 score of 95.49% by 10 runs over the test set. The sensitivities of the four classes were 95.44%, 96.07%, 94.41%, and 96.07%, respectively. The precisions of four classes were 96.45%, 95.22%, 95.05%, and 95.28%, respectively. The F1 scores of the four classes were 95.94%, 95.64%, 94.73%, and 95.67%, respectively. Besides, the confusion matrix was given. (Conclusions) The DRAPNet is effective in diagnosing COVID-19 and other chest infectious diseases. The RAP gives better results than four other methods: strided convolution, l2-norm pooling, average pooling, and max pooling. |
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Keywords: | COVID-19 rank-based average pooling deep learning deep neural network |
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