Learning without forgetting by leveraging transfer learning for detecting COVID-19 infection from CT images
Abstract COVID-19, a global pandemic, has killed thousands in the last three years. Pathogenic laboratory testing is the gold standard but has a high false-negative rate, making alternate diagnostic procedures necessary to fight against it. Computer Tomography (CT) scans help diagnose and monitor CO...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-34908-z |
| _version_ | 1797818029188317184 |
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| author | Malliga Subramanian Veerappampalayam Easwaramoorthy Sathishkumar Jaehyuk Cho Kogilavani Shanmugavadivel |
| author_facet | Malliga Subramanian Veerappampalayam Easwaramoorthy Sathishkumar Jaehyuk Cho Kogilavani Shanmugavadivel |
| author_sort | Malliga Subramanian |
| collection | DOAJ |
| description | Abstract COVID-19, a global pandemic, has killed thousands in the last three years. Pathogenic laboratory testing is the gold standard but has a high false-negative rate, making alternate diagnostic procedures necessary to fight against it. Computer Tomography (CT) scans help diagnose and monitor COVID-19, especially in severe cases. But, visual inspection of CT images takes time and effort. In this study, we employ Convolution Neural Network (CNN) to detect coronavirus infection from CT images. The proposed study utilized transfer learning on the three pre-trained deep CNN models, namely VGG-16, ResNet, and wide ResNet, to diagnose and detect COVID-19 infection from the CT images. However, when the pre-trained models are retrained, the model suffers the generalization capability to categorize the data in the original datasets. The novel aspect of this work is the integration of deep CNN architectures with Learning without Forgetting (LwF) to enhance the model’s generalization capabilities on both trained and new data samples. The LwF makes the network use its learning capabilities in training on the new dataset while preserving the original competencies. The deep CNN models with the LwF model are evaluated on original images and CT scans of individuals infected with Delta-variant of the SARS-CoV-2 virus. The experimental results show that of the three fine-tuned CNN models with the LwF method, the wide ResNet model’s performance is superior and effective in classifying original and delta-variant datasets with an accuracy of 93.08% and 92.32%, respectively. |
| first_indexed | 2024-03-13T09:02:13Z |
| format | Article |
| id | doaj.art-f875abc9dcee4f84a729741867f9cd0e |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-13T09:02:13Z |
| publishDate | 2023-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-f875abc9dcee4f84a729741867f9cd0e2023-05-28T11:16:41ZengNature PortfolioScientific Reports2045-23222023-05-0113111610.1038/s41598-023-34908-zLearning without forgetting by leveraging transfer learning for detecting COVID-19 infection from CT imagesMalliga Subramanian0Veerappampalayam Easwaramoorthy Sathishkumar1Jaehyuk Cho2Kogilavani Shanmugavadivel3Department of Computer Science and Engineering, Kongu Engineering CollegeDepartment of Software Engineering, Jeonbuk National UniversityDepartment of Software Engineering, Jeonbuk National UniversityDepartment of Computer Science and Engineering, Kongu Engineering CollegeAbstract COVID-19, a global pandemic, has killed thousands in the last three years. Pathogenic laboratory testing is the gold standard but has a high false-negative rate, making alternate diagnostic procedures necessary to fight against it. Computer Tomography (CT) scans help diagnose and monitor COVID-19, especially in severe cases. But, visual inspection of CT images takes time and effort. In this study, we employ Convolution Neural Network (CNN) to detect coronavirus infection from CT images. The proposed study utilized transfer learning on the three pre-trained deep CNN models, namely VGG-16, ResNet, and wide ResNet, to diagnose and detect COVID-19 infection from the CT images. However, when the pre-trained models are retrained, the model suffers the generalization capability to categorize the data in the original datasets. The novel aspect of this work is the integration of deep CNN architectures with Learning without Forgetting (LwF) to enhance the model’s generalization capabilities on both trained and new data samples. The LwF makes the network use its learning capabilities in training on the new dataset while preserving the original competencies. The deep CNN models with the LwF model are evaluated on original images and CT scans of individuals infected with Delta-variant of the SARS-CoV-2 virus. The experimental results show that of the three fine-tuned CNN models with the LwF method, the wide ResNet model’s performance is superior and effective in classifying original and delta-variant datasets with an accuracy of 93.08% and 92.32%, respectively.https://doi.org/10.1038/s41598-023-34908-z |
| spellingShingle | Malliga Subramanian Veerappampalayam Easwaramoorthy Sathishkumar Jaehyuk Cho Kogilavani Shanmugavadivel Learning without forgetting by leveraging transfer learning for detecting COVID-19 infection from CT images Scientific Reports |
| title | Learning without forgetting by leveraging transfer learning for detecting COVID-19 infection from CT images |
| title_full | Learning without forgetting by leveraging transfer learning for detecting COVID-19 infection from CT images |
| title_fullStr | Learning without forgetting by leveraging transfer learning for detecting COVID-19 infection from CT images |
| title_full_unstemmed | Learning without forgetting by leveraging transfer learning for detecting COVID-19 infection from CT images |
| title_short | Learning without forgetting by leveraging transfer learning for detecting COVID-19 infection from CT images |
| title_sort | learning without forgetting by leveraging transfer learning for detecting covid 19 infection from ct images |
| url | https://doi.org/10.1038/s41598-023-34908-z |
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