Early bacterial detection in bloodstream infection using deep transfer learning algorithm
An infection caused by bacteria can lead to severe complications affecting bloodstream disease. At present, blood cultures are used to identify bacteria. However, blood culture is a time-consuming and labor-intensive method of diagnosing disease. The effect of delayed early diagnosis is that it infl...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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iJOE
2023
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/37874/ http://umpir.ump.edu.my/id/eprint/37874/1/Early%20bacterial%20detection%20in%20bloodstream%20infection%20using%20deep%20transfer%20learning%20algorithm.pdf |
| _version_ | 1848825368644943872 |
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| author | Son Ali, Akbar Kamarul Hawari, Ghazali Habsah, Hasan Wahyu Sapto, Aji Yudhana, Anton |
| author_facet | Son Ali, Akbar Kamarul Hawari, Ghazali Habsah, Hasan Wahyu Sapto, Aji Yudhana, Anton |
| author_sort | Son Ali, Akbar |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | An infection caused by bacteria can lead to severe complications affecting bloodstream disease. At present, blood cultures are used to identify bacteria. However, blood culture is a time-consuming and labor-intensive method of diagnosing disease. The effect of delayed early diagnosis is that it influences the mortality risk. Thus, it is urgent to develop an initial prediction model to identify patients with bloodstream infections. This paper focused on classifying the bacteria using a deep learning approach. Besides, techniques of deep learning have the ability to enhance the bacterial classification process more effectively. Using the transfer learning-based convolutional neural network technique involved to develop our model. In addition, we compared the proposed model with another model used to find the best results. Compared to other models, the proposed model achieved an evaluation score with high accuracy of 98.62%. Medical decision-making may benefit from the proposed approach. |
| first_indexed | 2025-11-15T03:27:49Z |
| format | Article |
| id | ump-37874 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:27:49Z |
| publishDate | 2023 |
| publisher | iJOE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-378742023-06-27T03:21:12Z http://umpir.ump.edu.my/id/eprint/37874/ Early bacterial detection in bloodstream infection using deep transfer learning algorithm Son Ali, Akbar Kamarul Hawari, Ghazali Habsah, Hasan Wahyu Sapto, Aji Yudhana, Anton QH Natural history RA Public aspects of medicine TK Electrical engineering. Electronics Nuclear engineering An infection caused by bacteria can lead to severe complications affecting bloodstream disease. At present, blood cultures are used to identify bacteria. However, blood culture is a time-consuming and labor-intensive method of diagnosing disease. The effect of delayed early diagnosis is that it influences the mortality risk. Thus, it is urgent to develop an initial prediction model to identify patients with bloodstream infections. This paper focused on classifying the bacteria using a deep learning approach. Besides, techniques of deep learning have the ability to enhance the bacterial classification process more effectively. Using the transfer learning-based convolutional neural network technique involved to develop our model. In addition, we compared the proposed model with another model used to find the best results. Compared to other models, the proposed model achieved an evaluation score with high accuracy of 98.62%. Medical decision-making may benefit from the proposed approach. iJOE 2023-01 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/37874/1/Early%20bacterial%20detection%20in%20bloodstream%20infection%20using%20deep%20transfer%20learning%20algorithm.pdf Son Ali, Akbar and Kamarul Hawari, Ghazali and Habsah, Hasan and Wahyu Sapto, Aji and Yudhana, Anton (2023) Early bacterial detection in bloodstream infection using deep transfer learning algorithm. International Journal of Online and Biomedical Engineering (iJOE), 19 (1). 80 -92. ISSN 2626-8493. (Published) https://doi.org/10.3991/ijoe.v19i01.35047 https://doi.org/10.3991/ijoe.v19i01.35047 |
| spellingShingle | QH Natural history RA Public aspects of medicine TK Electrical engineering. Electronics Nuclear engineering Son Ali, Akbar Kamarul Hawari, Ghazali Habsah, Hasan Wahyu Sapto, Aji Yudhana, Anton Early bacterial detection in bloodstream infection using deep transfer learning algorithm |
| title | Early bacterial detection in bloodstream infection using deep transfer learning algorithm |
| title_full | Early bacterial detection in bloodstream infection using deep transfer learning algorithm |
| title_fullStr | Early bacterial detection in bloodstream infection using deep transfer learning algorithm |
| title_full_unstemmed | Early bacterial detection in bloodstream infection using deep transfer learning algorithm |
| title_short | Early bacterial detection in bloodstream infection using deep transfer learning algorithm |
| title_sort | early bacterial detection in bloodstream infection using deep transfer learning algorithm |
| topic | QH Natural history RA Public aspects of medicine TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/37874/ http://umpir.ump.edu.my/id/eprint/37874/ http://umpir.ump.edu.my/id/eprint/37874/ http://umpir.ump.edu.my/id/eprint/37874/1/Early%20bacterial%20detection%20in%20bloodstream%20infection%20using%20deep%20transfer%20learning%20algorithm.pdf |