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Learning without forgetting by leveraging transfer learning for detecting COVID-19 infection from CT images
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, e...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10211304/ https://www.ncbi.nlm.nih.gov/pubmed/37231044 http://dx.doi.org/10.1038/s41598-023-34908-z |
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author | Subramanian, Malliga Sathishkumar, Veerappampalayam Easwaramoorthy Cho, Jaehyuk Shanmugavadivel, Kogilavani |
author_facet | Subramanian, Malliga Sathishkumar, Veerappampalayam Easwaramoorthy Cho, Jaehyuk Shanmugavadivel, Kogilavani |
author_sort | Subramanian, Malliga |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10211304 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-102113042023-05-26 Learning without forgetting by leveraging transfer learning for detecting COVID-19 infection from CT images Subramanian, Malliga Sathishkumar, Veerappampalayam Easwaramoorthy Cho, Jaehyuk Shanmugavadivel, Kogilavani Sci Rep Article 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. Nature Publishing Group UK 2023-05-25 /pmc/articles/PMC10211304/ /pubmed/37231044 http://dx.doi.org/10.1038/s41598-023-34908-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Subramanian, Malliga Sathishkumar, Veerappampalayam Easwaramoorthy Cho, Jaehyuk Shanmugavadivel, Kogilavani Learning without forgetting by leveraging transfer learning for detecting COVID-19 infection from CT images |
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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10211304/ https://www.ncbi.nlm.nih.gov/pubmed/37231044 http://dx.doi.org/10.1038/s41598-023-34908-z |
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