Early Bacterial Detection in Bloodstream Infection using Deep Transfer Learning Algorithm

Authors

  • Son Ali Akbar Department of Electrical Engineering, Universitas Ahmad Dahlan, Yogyakarta, Indonesia https://orcid.org/0000-0001-8038-8815
  • Kamarul Hawari Ghazali Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang, Pahang, Malaysia https://orcid.org/0000-0002-3327-3502
  • Habsah Hasan School of Medical Sciences, Universiti Sains Malaysia, Kelantan, Malaysia https://orcid.org/0000-0002-4948-2157
  • Wahyu Sapto Aji Department of Electrical Engineering, Universitas Ahmad Dahlan, Yogyakarta, Indonesia https://orcid.org/0000-0002-5498-5090
  • Anton Yudhana Department of Electrical Engineering, Universitas Ahmad Dahlan, Yogyakarta, Indonesia

DOI:

https://doi.org/10.3991/ijoe.v19i01.35047

Keywords:

Transfer learning, Bacterial, Bloodstream disease, Convolutional neural network

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.

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Published

2023-01-17

How to Cite

Ali Akbar, S., Hawari Ghazali, K., Hasan, H. ., Sapto Aji, W., & Yudhana, A. (2023). Early Bacterial Detection in Bloodstream Infection using Deep Transfer Learning Algorithm. International Journal of Online and Biomedical Engineering (iJOE), 19(01), pp. 80–92. https://doi.org/10.3991/ijoe.v19i01.35047

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Papers