Early bacterial detection in bloodstream infection using deep transfer learning algorithm

• Main Authors: Son Ali, Akbar, Kamarul Hawari, Ghazali, Habsah, Hasan, Wahyu Sapto, Aji, Yudhana, Anton
• Format: Article
• Language: English
• Published: iJOE 2023
• Subjects: QH Natural history; RA Public aspects of medicine; TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://umpir.ump.edu.my/id/eprint/37874/1/Early%20bacterial%20detection%20in%20bloodstream%20infection%20using%20deep%20transfer%20learning%20algorithm.pdf; http://umpir.ump.edu.my/id/eprint/37874/; https://doi.org/10.3991/ijoe.v19i01.35047; https://doi.org/10.3991/ijoe.v19i01.35047

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.