@Article{cmes.2021.015807, AUTHOR = {Yudong Zhang, Xin Zhang, Weiguo Zhu}, TITLE = {ANC: Attention Network for COVID-19 Explainable Diagnosis Based on Convolutional Block Attention Module}, JOURNAL = {Computer Modeling in Engineering \& Sciences}, VOLUME = {127}, YEAR = {2021}, NUMBER = {3}, PAGES = {1037--1058}, URL = {http://www.techscience.com/CMES/v127n3/42609}, ISSN = {1526-1506}, 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.}, DOI = {10.32604/cmes.2021.015807} }