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)
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Abstract
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.
| Item Type: | Article |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Transfer learning; Bacterial; Bloodstream disease; Convolutional neural network |
| Subjects: | Q Science > QH Natural history R Medicine > RA Public aspects of medicine T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Electrical and Electronic Engineering Technology |
| Depositing User: | Mrs Norsaini Abdul Samat |
| Date Deposited: | 27 Jun 2023 03:21 |
| Last Modified: | 27 Jun 2023 03:21 |
| URI: | http://umpir.ump.edu.my/id/eprint/37874 |
| Download Statistic: | View Download Statistics |
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