GM-CBAM-ResNet: A Lightweight Deep Learning Network for Diagnosis of COVID-19
COVID-19 can cause acute infectious diseases of the respiratory system, and may probably lead to heart damage, which will seriously threaten human health. Electrocardiograms (ECGs) have the advantages of being low cost, non-invasive, and radiation free, and is widely used for evaluating heart health...
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MDPI AG
2025-03-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/11/3/76 |
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| author | Junjiang Zhu Yihui Zhang Cheng Ma Jiaming Wu Xuchen Wang Dongdong Kong |
| author_facet | Junjiang Zhu Yihui Zhang Cheng Ma Jiaming Wu Xuchen Wang Dongdong Kong |
| author_sort | Junjiang Zhu |
| collection | DOAJ |
| description | COVID-19 can cause acute infectious diseases of the respiratory system, and may probably lead to heart damage, which will seriously threaten human health. Electrocardiograms (ECGs) have the advantages of being low cost, non-invasive, and radiation free, and is widely used for evaluating heart health status. In this work, a lightweight deep learning network named GM-CBAM-ResNet is proposed for diagnosing COVID-19 based on ECG images. GM-CBAM-ResNet is constructed by replacing the convolution module with the Ghost module (GM) and adding the convolutional block attention module (CBAM) in the residual module of ResNet. To reveal the superiority of GM-CBAM-ResNet, the other three methods (ResNet, GM-ResNet, and CBAM-ResNet) are also analyzed from the following aspects: model performance, complexity, and interpretability. The model performance is evaluated by using the open ‘ECG Images dataset of Cardiac and COVID-19 Patients’. The complexity is reflected by comparing the number of model parameters. The interpretability is analyzed by utilizing Gradient-weighted Class Activation Mapping (Grad-CAM). Parameter statistics indicate that, on the basis of ResNet19, the number of model parameters of GM-CBAM-ResNet19 is reduced by 45.4%. Experimental results show that, under less model complexity, GM-CBAM-ResNet19 improves the diagnostic accuracy by approximately 5% in comparison with ResNet19. Additionally, the interpretability analysis shows that CBAM can suppress the interference of grid backgrounds and ensure higher diagnostic accuracy under lower model complexity. This work provides a lightweight solution for the rapid and accurate diagnosing of COVD-19 based on ECG images, which holds significant practical deployment value. |
| format | Article |
| id | doaj-art-81fb1d4f0f6143e5bb5fee98e339ecfa |
| institution | OA Journals |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-81fb1d4f0f6143e5bb5fee98e339ecfa2025-08-20T01:48:52ZengMDPI AGJournal of Imaging2313-433X2025-03-011137610.3390/jimaging11030076GM-CBAM-ResNet: A Lightweight Deep Learning Network for Diagnosis of COVID-19Junjiang Zhu0Yihui Zhang1Cheng Ma2Jiaming Wu3Xuchen Wang4Dongdong Kong5College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, ChinaCollege of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, ChinaCollege of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, ChinaSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaDepartment of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, ChinaSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaCOVID-19 can cause acute infectious diseases of the respiratory system, and may probably lead to heart damage, which will seriously threaten human health. Electrocardiograms (ECGs) have the advantages of being low cost, non-invasive, and radiation free, and is widely used for evaluating heart health status. In this work, a lightweight deep learning network named GM-CBAM-ResNet is proposed for diagnosing COVID-19 based on ECG images. GM-CBAM-ResNet is constructed by replacing the convolution module with the Ghost module (GM) and adding the convolutional block attention module (CBAM) in the residual module of ResNet. To reveal the superiority of GM-CBAM-ResNet, the other three methods (ResNet, GM-ResNet, and CBAM-ResNet) are also analyzed from the following aspects: model performance, complexity, and interpretability. The model performance is evaluated by using the open ‘ECG Images dataset of Cardiac and COVID-19 Patients’. The complexity is reflected by comparing the number of model parameters. The interpretability is analyzed by utilizing Gradient-weighted Class Activation Mapping (Grad-CAM). Parameter statistics indicate that, on the basis of ResNet19, the number of model parameters of GM-CBAM-ResNet19 is reduced by 45.4%. Experimental results show that, under less model complexity, GM-CBAM-ResNet19 improves the diagnostic accuracy by approximately 5% in comparison with ResNet19. Additionally, the interpretability analysis shows that CBAM can suppress the interference of grid backgrounds and ensure higher diagnostic accuracy under lower model complexity. This work provides a lightweight solution for the rapid and accurate diagnosing of COVD-19 based on ECG images, which holds significant practical deployment value.https://www.mdpi.com/2313-433X/11/3/76COVID-19ECG imagesghost module (GM)convolutional block attention module (CBAM)lightweightdeep learning |
| spellingShingle | Junjiang Zhu Yihui Zhang Cheng Ma Jiaming Wu Xuchen Wang Dongdong Kong GM-CBAM-ResNet: A Lightweight Deep Learning Network for Diagnosis of COVID-19 Journal of Imaging COVID-19 ECG images ghost module (GM) convolutional block attention module (CBAM) lightweight deep learning |
| title | GM-CBAM-ResNet: A Lightweight Deep Learning Network for Diagnosis of COVID-19 |
| title_full | GM-CBAM-ResNet: A Lightweight Deep Learning Network for Diagnosis of COVID-19 |
| title_fullStr | GM-CBAM-ResNet: A Lightweight Deep Learning Network for Diagnosis of COVID-19 |
| title_full_unstemmed | GM-CBAM-ResNet: A Lightweight Deep Learning Network for Diagnosis of COVID-19 |
| title_short | GM-CBAM-ResNet: A Lightweight Deep Learning Network for Diagnosis of COVID-19 |
| title_sort | gm cbam resnet a lightweight deep learning network for diagnosis of covid 19 |
| topic | COVID-19 ECG images ghost module (GM) convolutional block attention module (CBAM) lightweight deep learning |
| url | https://www.mdpi.com/2313-433X/11/3/76 |
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