An emerging network for COVID-19 CT-scan classification using an ensemble deep transfer learning model
Over the past few years, the widespread outbreak of COVID-19 has caused the death of millions of people worldwide. Early diagnosis of the virus is essential to control its spread and provide timely treatment. Artificial intelligence methods are often used as powerful tools to reach a COVID-19 diagno...
| Main Authors: | , , , , , |
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| Format: | Article |
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Elsevier B.V.
2024
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| _version_ | 1824452057763938304 |
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| author | Yousefpanah, Kolsoum Ebadi, M. J. Sabzekar, Sina Zakaria, Nor Hidayati Osman, Nurul Aida Ali Ahmadian, Ali Ahmadian |
| author_facet | Yousefpanah, Kolsoum Ebadi, M. J. Sabzekar, Sina Zakaria, Nor Hidayati Osman, Nurul Aida Ali Ahmadian, Ali Ahmadian |
| author_sort | Yousefpanah, Kolsoum |
| collection | ePrints |
| description | Over the past few years, the widespread outbreak of COVID-19 has caused the death of millions of people worldwide. Early diagnosis of the virus is essential to control its spread and provide timely treatment. Artificial intelligence methods are often used as powerful tools to reach a COVID-19 diagnosis via computed tomography (CT) samples. In this paper, artificial intelligence-based methods are introduced to diagnose COVID-19. At first, a network called CT6-CNN is designed, and then two ensemble deep transfer learning models are developed based on Xception, ResNet-101, DenseNet-169, and CT6-CNN to reach a COVID-19 diagnosis by CT samples. The publicly available SARS-CoV-2 CT dataset is utilized for our implementation, including 2481 CT scans. The dataset is separated into 2108, 248, and 125 images for training, validation, and testing, respectively. Based on experimental results, the CT6-CNN model achieved 94.66% accuracy, 94.67% precision, 94.67% sensitivity, and 94.65% F1-score rate. Moreover, the ensemble learning models reached 99.2% accuracy. Experimental results affirm the effectiveness of designed models, especially the ensemble deep learning models, to reach a diagnosis of COVID-19. |
| first_indexed | 2025-02-19T02:44:28Z |
| format | Article |
| id | utm.eprints-108879 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2025-02-19T02:44:28Z |
| publishDate | 2024 |
| publisher | Elsevier B.V. |
| record_format | dspace |
| spelling | utm.eprints-1088792024-12-11T09:39:52Z http://eprints.utm.my/108879/ An emerging network for COVID-19 CT-scan classification using an ensemble deep transfer learning model Yousefpanah, Kolsoum Ebadi, M. J. Sabzekar, Sina Zakaria, Nor Hidayati Osman, Nurul Aida Ali Ahmadian, Ali Ahmadian HD30.2 Knowledge management Over the past few years, the widespread outbreak of COVID-19 has caused the death of millions of people worldwide. Early diagnosis of the virus is essential to control its spread and provide timely treatment. Artificial intelligence methods are often used as powerful tools to reach a COVID-19 diagnosis via computed tomography (CT) samples. In this paper, artificial intelligence-based methods are introduced to diagnose COVID-19. At first, a network called CT6-CNN is designed, and then two ensemble deep transfer learning models are developed based on Xception, ResNet-101, DenseNet-169, and CT6-CNN to reach a COVID-19 diagnosis by CT samples. The publicly available SARS-CoV-2 CT dataset is utilized for our implementation, including 2481 CT scans. The dataset is separated into 2108, 248, and 125 images for training, validation, and testing, respectively. Based on experimental results, the CT6-CNN model achieved 94.66% accuracy, 94.67% precision, 94.67% sensitivity, and 94.65% F1-score rate. Moreover, the ensemble learning models reached 99.2% accuracy. Experimental results affirm the effectiveness of designed models, especially the ensemble deep learning models, to reach a diagnosis of COVID-19. Elsevier B.V. 2024 Article PeerReviewed Yousefpanah, Kolsoum and Ebadi, M. J. and Sabzekar, Sina and Zakaria, Nor Hidayati and Osman, Nurul Aida and Ali Ahmadian, Ali Ahmadian (2024) An emerging network for COVID-19 CT-scan classification using an ensemble deep transfer learning model. Acta Tropica, 257 (NA). NA-NA. ISSN 0001-706X http://dx.doi.org/10.1016/j.actatropica.2024.107277 DOI : 10.1016/j.actatropica.2024.107277 |
| spellingShingle | HD30.2 Knowledge management Yousefpanah, Kolsoum Ebadi, M. J. Sabzekar, Sina Zakaria, Nor Hidayati Osman, Nurul Aida Ali Ahmadian, Ali Ahmadian An emerging network for COVID-19 CT-scan classification using an ensemble deep transfer learning model |
| title | An emerging network for COVID-19 CT-scan classification using an ensemble deep transfer learning model |
| title_full | An emerging network for COVID-19 CT-scan classification using an ensemble deep transfer learning model |
| title_fullStr | An emerging network for COVID-19 CT-scan classification using an ensemble deep transfer learning model |
| title_full_unstemmed | An emerging network for COVID-19 CT-scan classification using an ensemble deep transfer learning model |
| title_short | An emerging network for COVID-19 CT-scan classification using an ensemble deep transfer learning model |
| title_sort | emerging network for covid 19 ct scan classification using an ensemble deep transfer learning model |
| topic | HD30.2 Knowledge management |
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