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ANC: Attention Network for COVID-19 Explainable Diagnosis Based on Convolutional Block Attention Module
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ANC: Attention Network for COVID-19 Explainable Diagnosis Based on Convolutional Block Attention Module
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Journal Article
ANC: Attention Network for COVID-19 Explainable Diagnosis Based on Convolutional Block Attention Module
2021
Overview
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 over tting. 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.
Publisher
Tech Science Press
