摘要
Aim: To diagnose COVID-19 more efficiently and more correctly, this study proposed a novel attention network forCOVID-19 (ANC). Methods: Two datasets were used in this study. An 18-way data augmentation was proposed toavoid overfitting. Then, convolutional block attention module (CBAM) was integrated to our model, the structureof which is fine-tuned. Finally, Grad-CAM was used to provide an explainable diagnosis. Results: The accuracyof our ANC methods on two datasets are 96.32% ± 1.06%, and 96.00% ± 1.03%, respectively. Conclusions: Thisproposed ANC method is superior to 9 state-of-the-art approaches.
基金
This paper is partially supported by 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.
