Yudong Zhang1,3,*, Xin Zhang2,*, Weiguo Zhu1
CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.3, pp. 1037-1058, 2021, DOI:10.32604/cmes.2021.015807
Abstract Aim: To diagnose COVID-19 more efficiently and more correctly, this study proposed a novel attention network for
COVID-19 (ANC). Methods: Two datasets were used in this study. An 18-way data augmentation was proposed to
avoid overfitting. Then, convolutional block attention module (CBAM) was integrated to our model, the structure
of which is fine-tuned. Finally, Grad-CAM was used to provide an explainable diagnosis. Results: The accuracy
of our ANC methods on two datasets are 96.32% ± 1.06%, and 96.00% ± 1.03%, respectively. Conclusions: This
proposed ANC method is superior to 9 state-of-the-art approaches. More >
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