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

被引：43
|
作者：
Zhang, Yudong [1 ,3 ]
Zhang, Xin [2 ]
Zhu, Weiguo [1 ]
机构：
[1] Huaiyin Inst Technol, Jiangsu Key Lab Adv Mfg Technol, Huaian 223003, Peoples R China
[2] Fourth Peoples Hosp Huaian, Dept Med Imaging, Huaian 223002, Peoples R China
[3] Univ Leicester, Sch Informat, Leicester LE1 7RH, Leics, England
来源：
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES | 2021年 / 127卷 / 03期
基金：
英国医学研究理事会;
关键词：
Deep learning; convolutional block attention module; attention mechanism; COVID-19; explainable diagnosis; CLASSIFICATION;
D O I：
10.32604/cmes.2021.015807
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
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.
引用
收藏
页码：1037 / 1058
页数：22
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