期刊
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
卷 127, 期 3, 页码 1037-1058出版社
TECH SCIENCE PRESS
DOI: 10.32604/cmes.2021.015807
关键词
Deep learning; convolutional block attention module; attention mechanism; COVID-19; explainable diagnosis
资金
- Open Fund for Jiangsu Key Laboratory of Advanced Manufacturing Technology [HGAMTL-1703]
- Guangxi Key Laboratory of Trusted Software [kx201901]
- Fundamental Research Funds for the Central Universities [CDLS-2020-03]
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education
- Royal Society International Exchanges Cost Share Award, UK [RP202G0230]
- Medical Research Council Confidence in Concept Award, UK [MC_PC_17171]
- Hope Foundation for Cancer Research, UK [RM60G0680]
- British Heart Foundation Accelerator Award, UK
The proposed ANC method achieved high accuracy in diagnosing COVID-19, outperforming 9 state-of-the-art approaches. By integrating CBAM and Grad-CAM, the diagnostic interpretability was improved, and overfitting was avoided.
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.
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