Open Access


ANC: Attention Network for COVID-19 Explainable Diagnosis Based on Convolutional Block Attention Module

Yudong Zhang1,3,*, Xin Zhang2,*, Weiguo Zhu1
1 Jiangsu Key Laboratory of Advanced Manufacturing Technology, Huaiyin Institute of Technology, Huai’an, 223003, China
2 Department of Medical Imaging, The Fourth People’s Hospital of Huai’an, Huai’an, 223002, China
3 School of Informatics, University of Leicester, Leicester, LE1 7RH, UK
* Corresponding Authors: Yudong Zhang. Email: ; Xin Zhang. Email:
(This article belongs to this Special Issue: Computer-Assisted Imaging Processing and Machine Learning Applications on Diagnosis of Chest Radiograph)

Computer Modeling in Engineering & Sciences 2021, 127(3), 1037-1058.

Received 15 January 2021; Accepted 24 February 2021; Issue published 24 May 2021


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.


Deep learning; convolutional block attention module; attention mechanism; COVID-19; explainable diagnosis

Cite This Article

Zhang, Y., Zhang, X., Zhu, W. (2021). ANC: Attention Network for COVID-19 Explainable Diagnosis Based on Convolutional Block Attention Module. CMES-Computer Modeling in Engineering & Sciences, 127(3), 1037–1058.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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