COVID-19 disease identification network based on weakly supervised feature selection

Jingyao Liu; Qinghe Feng; Yu Miao; Wei He; Weili Shi; Zhengang Jiang; Jingyao Liu; Qinghe Feng; Yu Miao; Wei He; Weili Shi; Zhengang Jiang

doi:10.3934/mbe.2023409

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 5: 9327-9348. doi: 10.3934/mbe.2023409

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COVID-19 disease identification network based on weakly supervised feature selection

1.
School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, Jilin 130022, China
2.
School of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, China
3.
Zhongshan Institute of Changchun University of Science and Technology, Zhongshan 528437, China
4.
School of Intelligent Engineering, Henan Institute of Technology, Xinxiang 453003, China

Academic Editor: Yang Kuang

Received: 13 January 2023 Revised: 23 February 2023 Accepted: 06 March 2023 Published: 16 March 2023

The coronavirus disease 2019 (COVID-19) outbreak has resulted in countless infections and deaths worldwide, posing increasing challenges for the health care system. The use of artificial intelligence to assist in diagnosis not only had a high accuracy rate but also saved time and effort in the sudden outbreak phase with the lack of doctors and medical equipment. This study aimed to propose a weakly supervised COVID-19 classification network (W-COVNet). This network was divided into three main modules: weakly supervised feature selection module (W-FS), deep learning bilinear feature fusion module (DBFF) and Grad-CAM++ based network visualization module (Grad-Ⅴ). The first module, W-FS, mainly removed redundant background features from computed tomography (CT) images, performed feature selection and retained core feature regions. The second module, DBFF, mainly used two symmetric networks to extract different features and thus obtain rich complementary features. The third module, Grad-Ⅴ, allowed the visualization of lesions in unlabeled images. A fivefold cross-validation experiment showed an average classification accuracy of 85.3%, and a comparison with seven advanced classification models showed that our proposed network had a better performance.
- Classification,
- COVID-19,
- deep learning,
- feature selection,
- weakly supervised
Citation: Jingyao Liu, Qinghe Feng, Yu Miao, Wei He, Weili Shi, Zhengang Jiang. COVID-19 disease identification network based on weakly supervised feature selection[J]. Mathematical Biosciences and Engineering, 2023, 20(5): 9327-9348. doi: 10.3934/mbe.2023409

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Abstract

The coronavirus disease 2019 (COVID-19) outbreak has resulted in countless infections and deaths worldwide, posing increasing challenges for the health care system. The use of artificial intelligence to assist in diagnosis not only had a high accuracy rate but also saved time and effort in the sudden outbreak phase with the lack of doctors and medical equipment. This study aimed to propose a weakly supervised COVID-19 classification network (W-COVNet). This network was divided into three main modules: weakly supervised feature selection module (W-FS), deep learning bilinear feature fusion module (DBFF) and Grad-CAM++ based network visualization module (Grad-Ⅴ). The first module, W-FS, mainly removed redundant background features from computed tomography (CT) images, performed feature selection and retained core feature regions. The second module, DBFF, mainly used two symmetric networks to extract different features and thus obtain rich complementary features. The third module, Grad-Ⅴ, allowed the visualization of lesions in unlabeled images. A fivefold cross-validation experiment showed an average classification accuracy of 85.3%, and a comparison with seven advanced classification models showed that our proposed network had a better performance.

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