Early Bacterial Detection in Bloodstream Infection using Deep Transfer Learning Algorithm
DOI:
https://doi.org/10.3991/ijoe.v19i01.35047Keywords:
Transfer learning, Bacterial, Bloodstream disease, Convolutional neural networkAbstract
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.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Son Ali Akbar, Kamarul Hawari Ghazali, Habsah Hasan, Zeehaida Mohamed, Wahyu Sapto Aji
This work is licensed under a Creative Commons Attribution 4.0 International License.
The submitting author warrants that the submission is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution Licence (CC-BY What does this mean?). This is to get more legal certainty about what readers can do with published articles, and thus a wider dissemination and archiving, which in turn makes publishing with this journal more valuable for you, the authors.
By submitting an article the author grants to this journal the non-exclusive right to publish it. The author retains the copyright and the publishing rights for his article without any restrictions.
