The employment of transfer learning for Covid-19 diagnostics: A ResNet evaluation
Artificial intelligence (AI) have made significant gains and contribution, particularly owing to the introduction of powerful graphical processing units in recent times. Furthermore, the advent of transfer learning models, which is a subset of deep learning models such as VGG16, InceptionV3, and Res...
| Main Authors: | , , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/36979/1/The%20employment%20of%20transfer%20learning%20for%20covid-19%20diagnostics%20_%20A%20resnet%20evaluation.pdf |
| _version_ | 1825814848736329728 |
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| author | Amiir Haamzah, Mohamed Ismail Anwar, P. P. Abdul Majeed E. H., Yap A. H. P., Tan Mohd Azraai, Mohd Razman Ismail, Mohd Khairuddin M. A., Abdullah |
| author_facet | Amiir Haamzah, Mohamed Ismail Anwar, P. P. Abdul Majeed E. H., Yap A. H. P., Tan Mohd Azraai, Mohd Razman Ismail, Mohd Khairuddin M. A., Abdullah |
| author_sort | Amiir Haamzah, Mohamed Ismail |
| collection | UMP |
| description | Artificial intelligence (AI) have made significant gains and contribution, particularly owing to the introduction of powerful graphical processing units in recent times. Furthermore, the advent of transfer learning models, which is a subset of deep learning models such as VGG16, InceptionV3, and ResNet, to name a few, have further allowed for the accomplishment of a variety of tasks. In modern medicine, AI has been used for the detection of diseases. The recent virus outbreak has gravitated the capabilities of AI to be deployed in a short time as the virus evolves. This study demonstrated that medical data is sensitive, and the learning model should be tuned for each dataset. The findings from the present study suggest that the ResNet101V2-sigmoid pipeline shows the most promising results in detecting COVID-19 from chest x-ray images. This will pave the way for the development of high-performance detection models, albeit with limited datasets. |
| first_indexed | 2024-03-06T13:04:40Z |
| format | Conference or Workshop Item |
| id | UMPir36979 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T13:04:40Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | UMPir369792023-02-17T07:53:13Z http://umpir.ump.edu.my/id/eprint/36979/ The employment of transfer learning for Covid-19 diagnostics: A ResNet evaluation Amiir Haamzah, Mohamed Ismail Anwar, P. P. Abdul Majeed E. H., Yap A. H. P., Tan Mohd Azraai, Mohd Razman Ismail, Mohd Khairuddin M. A., Abdullah T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Artificial intelligence (AI) have made significant gains and contribution, particularly owing to the introduction of powerful graphical processing units in recent times. Furthermore, the advent of transfer learning models, which is a subset of deep learning models such as VGG16, InceptionV3, and ResNet, to name a few, have further allowed for the accomplishment of a variety of tasks. In modern medicine, AI has been used for the detection of diseases. The recent virus outbreak has gravitated the capabilities of AI to be deployed in a short time as the virus evolves. This study demonstrated that medical data is sensitive, and the learning model should be tuned for each dataset. The findings from the present study suggest that the ResNet101V2-sigmoid pipeline shows the most promising results in detecting COVID-19 from chest x-ray images. This will pave the way for the development of high-performance detection models, albeit with limited datasets. 2022-11-15 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/36979/1/The%20employment%20of%20transfer%20learning%20for%20covid-19%20diagnostics%20_%20A%20resnet%20evaluation.pdf Amiir Haamzah, Mohamed Ismail and Anwar, P. P. Abdul Majeed and E. H., Yap and A. H. P., Tan and Mohd Azraai, Mohd Razman and Ismail, Mohd Khairuddin and M. A., Abdullah (2022) The employment of transfer learning for Covid-19 diagnostics: A ResNet evaluation. In: The 6th National Conference for Postgraduate Research (NCON-PGR 2022) , 15 November 2022 , Virtual Conference, Universiti Malaysia Pahang, Malaysia. p. 128.. (Published) https://ncon-pgr.ump.edu.my/index.php/en/?option=com_fileman&view=file&routed=1&name=E-BOOK%20NCON%202022%20.pdf&folder=E-BOOK%20NCON%202022&container=fileman-files |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Amiir Haamzah, Mohamed Ismail Anwar, P. P. Abdul Majeed E. H., Yap A. H. P., Tan Mohd Azraai, Mohd Razman Ismail, Mohd Khairuddin M. A., Abdullah The employment of transfer learning for Covid-19 diagnostics: A ResNet evaluation |
| title | The employment of transfer learning for Covid-19 diagnostics: A ResNet evaluation |
| title_full | The employment of transfer learning for Covid-19 diagnostics: A ResNet evaluation |
| title_fullStr | The employment of transfer learning for Covid-19 diagnostics: A ResNet evaluation |
| title_full_unstemmed | The employment of transfer learning for Covid-19 diagnostics: A ResNet evaluation |
| title_short | The employment of transfer learning for Covid-19 diagnostics: A ResNet evaluation |
| title_sort | employment of transfer learning for covid 19 diagnostics a resnet evaluation |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/36979/1/The%20employment%20of%20transfer%20learning%20for%20covid-19%20diagnostics%20_%20A%20resnet%20evaluation.pdf |
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