The diagnosis of COVID-19 through X-ray images via transfer learning and fine-tuned dense layer on pipeline
X-ray is used in medical treatment as a method to diagnose the human body internally from diseases. Nevertheless, the development in machine learning technologies for pattern recognition have allowed machine learning of diagnosing diseases from chest X-ray images. One such diseases that are able to...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Penerbit UMP
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/33970/ http://umpir.ump.edu.my/id/eprint/33970/1/The%20diagnosis%20of%20COVID19%20through%20xray%20images.pdf |
| _version_ | 1848824389925076992 |
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| author | Amiir Haamzah, Mohamed Ismail Mohd Azraai, Mohd Razman Ismail, Mohd Khairuddin Muhammad Amirul, Abdullah Rabiu Muazu, Musa Anwar P. P., Abdul Majeed |
| author_facet | Amiir Haamzah, Mohamed Ismail Mohd Azraai, Mohd Razman Ismail, Mohd Khairuddin Muhammad Amirul, Abdullah Rabiu Muazu, Musa Anwar P. P., Abdul Majeed |
| author_sort | Amiir Haamzah, Mohamed Ismail |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | X-ray is used in medical treatment as a method to diagnose the human body internally from diseases. Nevertheless, the development in machine learning technologies for pattern recognition have allowed machine learning of diagnosing diseases from chest X-ray images. One such diseases that are able to be detected by using X-ray is the COVID-19 coronavirus. This research investigates the diagnosis of COVID-19 through X-ray images by using transfer learning and fine-tuning of the fully connected layer. Next, hyperparameters such as dropout, p, number of neurons, and activation functions are investigated on which combinations of these hyperparameters will yield the highest classification accuracy model. InceptionV3 which is one of the common neural network is used for feature extraction from chest X-ray images. Subsequently, the loss and accuracy graphs are used to find the pipeline which performs the best in classification task. The findings in this research will open new possibilities in screening method for COVID-19. |
| first_indexed | 2025-11-15T03:12:16Z |
| format | Article |
| id | ump-33970 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:12:16Z |
| publishDate | 2021 |
| publisher | Penerbit UMP |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-339702022-05-09T03:42:23Z http://umpir.ump.edu.my/id/eprint/33970/ The diagnosis of COVID-19 through X-ray images via transfer learning and fine-tuned dense layer on pipeline Amiir Haamzah, Mohamed Ismail Mohd Azraai, Mohd Razman Ismail, Mohd Khairuddin Muhammad Amirul, Abdullah Rabiu Muazu, Musa Anwar P. P., Abdul Majeed RA Public aspects of medicine TJ Mechanical engineering and machinery X-ray is used in medical treatment as a method to diagnose the human body internally from diseases. Nevertheless, the development in machine learning technologies for pattern recognition have allowed machine learning of diagnosing diseases from chest X-ray images. One such diseases that are able to be detected by using X-ray is the COVID-19 coronavirus. This research investigates the diagnosis of COVID-19 through X-ray images by using transfer learning and fine-tuning of the fully connected layer. Next, hyperparameters such as dropout, p, number of neurons, and activation functions are investigated on which combinations of these hyperparameters will yield the highest classification accuracy model. InceptionV3 which is one of the common neural network is used for feature extraction from chest X-ray images. Subsequently, the loss and accuracy graphs are used to find the pipeline which performs the best in classification task. The findings in this research will open new possibilities in screening method for COVID-19. Penerbit UMP 2021 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/33970/1/The%20diagnosis%20of%20COVID19%20through%20xray%20images.pdf Amiir Haamzah, Mohamed Ismail and Mohd Azraai, Mohd Razman and Ismail, Mohd Khairuddin and Muhammad Amirul, Abdullah and Rabiu Muazu, Musa and Anwar P. P., Abdul Majeed (2021) The diagnosis of COVID-19 through X-ray images via transfer learning and fine-tuned dense layer on pipeline. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 3 (2). pp. 19-24. ISSN 2637-0883. (Published) https://doi.org/10.15282/mekatronika.v3i2.7161 https://doi.org/10.15282/mekatronika.v3i2.7161 |
| spellingShingle | RA Public aspects of medicine TJ Mechanical engineering and machinery Amiir Haamzah, Mohamed Ismail Mohd Azraai, Mohd Razman Ismail, Mohd Khairuddin Muhammad Amirul, Abdullah Rabiu Muazu, Musa Anwar P. P., Abdul Majeed The diagnosis of COVID-19 through X-ray images via transfer learning and fine-tuned dense layer on pipeline |
| title | The diagnosis of COVID-19 through X-ray images via transfer learning and fine-tuned dense layer on pipeline |
| title_full | The diagnosis of COVID-19 through X-ray images via transfer learning and fine-tuned dense layer on pipeline |
| title_fullStr | The diagnosis of COVID-19 through X-ray images via transfer learning and fine-tuned dense layer on pipeline |
| title_full_unstemmed | The diagnosis of COVID-19 through X-ray images via transfer learning and fine-tuned dense layer on pipeline |
| title_short | The diagnosis of COVID-19 through X-ray images via transfer learning and fine-tuned dense layer on pipeline |
| title_sort | diagnosis of covid-19 through x-ray images via transfer learning and fine-tuned dense layer on pipeline |
| topic | RA Public aspects of medicine TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/33970/ http://umpir.ump.edu.my/id/eprint/33970/ http://umpir.ump.edu.my/id/eprint/33970/ http://umpir.ump.edu.my/id/eprint/33970/1/The%20diagnosis%20of%20COVID19%20through%20xray%20images.pdf |