A Deep Learning Model with Self-Supervised Learning and Attention Mechanism for COVID-19 Diagnosis Using Chest X-ray Images
Open accessAbstract
The SARS-CoV-2 virus has spread worldwide, and the World Health Organization has declared COVID-19 pandemic, proclaiming that the entire world must overcome it together. The chest X-ray and computed tomography datasets of individuals with COVID-19 remain limited, which can cause lower performance of deep learning model. In this study, we developed a model for the diagnosis of COVID-19 by solving the classification problem using a self-supervised learning technique with a convolution attention module. Self-supervised learning using a U-shaped convolutional neural network model combined with a convolution block attention module (CBAM) using over 100,000 chest X-Ray images with structure similarity (SSIM) index captures image representations extremely well. The system we proposed consists of fine-tuning the weights of the encoder after a self-supervised learning pretext task, interpreting the chest X-ray representation in the encoder using convolutional layers, and diagnosing the chest X-ray image as the classification model. Additionally, considering the CBAM further improves the averaged accuracy of 98.6%, thereby outperforming the baseline model (97.8%) by 0.8%. The proposed model classifies the three classes of normal, pneumonia, and COVID-19 extremely accurately, along with other metrics such as specificity and sensitivity that are similar to accuracy. The average area under the curve (AUC) is 0.994 in the COVID-19 class, indicating that our proposed model exhibits outstanding classification performance.
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Two of the five self-prediction studies compared their approach to models without self-supervised pre-training and reported slight relative improvements in performance of 1.12% and 0.690%.
“Two of the five studies compared their approach to models without self-supervised pre-training and reported slight relative improvements in performance of 1.12%67 and 0.690%63.”
From Self-supervised learning for medical image classification: a systematic review and implementation guidelines (2023) · cites this paper as [67]
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Research works printed but not linked: 1
- Zhai, X.; Oliver, A.; Kolesnikov, A.; Beyer, L. S4l: Self-supervised semi-supervised learning. In Proceedings of the IEEE International Conference on Computer Vision, Seoul, Korea, 27 October-3 November 2019; pp. 1476-1485
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Sorted by citations
Huang, S.-C., Pareek, A., Jensen, M., Lungren, M. P., Yeung, S., & Chaudhari, A. S. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00811-0 · Cited by 479 · see citing works
“Two of the fi ve studies compared their approach to models without self-supervised pre-training and reported slight relative improvements in performance of 1.12% 67 and 0.690% 63 .”
BackgroundResultInfluential
- Authors
- Park, J., Kwak, I.-Y., & Lim, C.
- Affiliations
- Chung-Ang University (KR)
- Corresponding author
- Lim, Changwon
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2021-08-18
- Volume
- 10
- Issue
- 16
- Pages
- 1996
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W3193757158
- Cited by
- 24
- References
- 57
- Citations by year
- 2025: 10 · 2024: 3 · 2023: 4 · 2022: 7
- FWCI
- 1.86
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Electronics (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Research Foundation of Korea (NRF-2017M3C4A7083281, 2020R1C1C1A01013020)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- COVID-19 diagnosis, chest X-ray images, self-supervised learning, attention mechanism, convolution block attention module, structural similarity, pneumonia classification, encoder fine-tuning, medical image classification
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