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