@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}
}