TY - EJOU AU - Zhang, Yudong AU - Zhang, Xin AU - Zhu, Weiguo TI - ANC: Attention Network for COVID-19 Explainable Diagnosis Based on Convolutional Block Attention Module T2 - Computer Modeling in Engineering \& Sciences PY - 2021 VL - 127 IS - 3 SN - 1526-1506 AB - 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. KW - Deep learning; convolutional block attention module; attention mechanism; COVID-19; explainable diagnosis DO - 10.32604/cmes.2021.015807