COVID-19 Detection in CT Images using Deep Transfer Learning
Confronting the COVID-19 pandemic introduced by newest corona virus, SARS-CoV-2, is one of the human species' most influential problems today. The fast identification and isolation of infected patients is a crucial factor in slowing down the spread of the virus. The Reverse Transcription Polym...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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International Transactions on Electrical Engineering and Computer Science
2022-09-01
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| Series: | International Transactions on Electrical Engineering and Computer Science |
| Subjects: | |
| Online Access: | https://iteecs.com/index.php/iteecs/article/view/6 |
| _version_ | 1797372885414707200 |
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| author | A. Anbarasi K. C. Nithyasree |
| author_facet | A. Anbarasi K. C. Nithyasree |
| author_sort | A. Anbarasi |
| collection | DOAJ |
| description |
Confronting the COVID-19 pandemic introduced by newest corona virus, SARS-CoV-2, is one of the human species' most influential problems today. The fast identification and isolation of infected patients is a crucial factor in slowing down the spread of the virus. The Reverse Transcription Polymerase Chain Reaction (RT-PCR) process, one of the basic methods for COVID-19 recognition, is time-consuming in addition short-lived due to the pandemic. Deep learning applied to patients' CT images has given away hopeful results popular the identification of COVID-19 in this context. The powerful net family of CNN models using CT images to perform COVID-19 recognition is suggested in this article by VGG-16. As a consequence, COVID-19 detection was proposed as a VGG-16 model with an overall accuracy of 98.33 percent. We assume that, both in terms of productivity and efficiency, the published figures reflect modern outcomes.
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| first_indexed | 2024-03-08T18:42:18Z |
| format | Article |
| id | doaj.art-c53c9679795c41e1b25e5235ab460f85 |
| institution | Directory Open Access Journal |
| issn | 2583-6471 |
| language | English |
| last_indexed | 2024-03-08T18:42:18Z |
| publishDate | 2022-09-01 |
| publisher | International Transactions on Electrical Engineering and Computer Science |
| record_format | Article |
| series | International Transactions on Electrical Engineering and Computer Science |
| spelling | doaj.art-c53c9679795c41e1b25e5235ab460f852023-12-29T04:46:25ZengInternational Transactions on Electrical Engineering and Computer ScienceInternational Transactions on Electrical Engineering and Computer Science2583-64712022-09-01116COVID-19 Detection in CT Images using Deep Transfer LearningA. Anbarasi0K. C. Nithyasree1Department of Computer Science, School of Engineering and Technology, Pondicherry University, Pondicherry, 605014, India.Department of Computer Science and Engineering, Rajiv Gandhi College of Engineering and Technology, Pondicherry, 607403, India. Confronting the COVID-19 pandemic introduced by newest corona virus, SARS-CoV-2, is one of the human species' most influential problems today. The fast identification and isolation of infected patients is a crucial factor in slowing down the spread of the virus. The Reverse Transcription Polymerase Chain Reaction (RT-PCR) process, one of the basic methods for COVID-19 recognition, is time-consuming in addition short-lived due to the pandemic. Deep learning applied to patients' CT images has given away hopeful results popular the identification of COVID-19 in this context. The powerful net family of CNN models using CT images to perform COVID-19 recognition is suggested in this article by VGG-16. As a consequence, COVID-19 detection was proposed as a VGG-16 model with an overall accuracy of 98.33 percent. We assume that, both in terms of productivity and efficiency, the published figures reflect modern outcomes. https://iteecs.com/index.php/iteecs/article/view/6VGG-16Deep learningCT imagesConvolution neural networks (CNN) |
| spellingShingle | A. Anbarasi K. C. Nithyasree COVID-19 Detection in CT Images using Deep Transfer Learning International Transactions on Electrical Engineering and Computer Science VGG-16 Deep learning CT images Convolution neural networks (CNN) |
| title | COVID-19 Detection in CT Images using Deep Transfer Learning |
| title_full | COVID-19 Detection in CT Images using Deep Transfer Learning |
| title_fullStr | COVID-19 Detection in CT Images using Deep Transfer Learning |
| title_full_unstemmed | COVID-19 Detection in CT Images using Deep Transfer Learning |
| title_short | COVID-19 Detection in CT Images using Deep Transfer Learning |
| title_sort | covid 19 detection in ct images using deep transfer learning |
| topic | VGG-16 Deep learning CT images Convolution neural networks (CNN) |
| url | https://iteecs.com/index.php/iteecs/article/view/6 |
| work_keys_str_mv | AT aanbarasi covid19detectioninctimagesusingdeeptransferlearning AT kcnithyasree covid19detectioninctimagesusingdeeptransferlearning |