A Deep Learning Model for Bacterial Classification Using Big Transfer (BiT)

Identification and classification of bacterial genera and species are very important for medical prevention, diagnosis, and treatment. However, due to microbial diversity and high variability in appearance, the manual classification of bacteria is a challenging and time-consuming task. This paper ai...

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Bibliographic Details
Published in:	IEEE access Vol. 12; pp. 15609 - 15621
Main Authors:	Visitsattaponge, Sarinporn, Bunkum, Manao, Pintavirooj, Chuchart, Paing, May Phu
Format:	Journal Article
Language:	English
Published:	Piscataway The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024 IEEE
Subjects:	Bacteria Bacteria classification big transfer Classification Data augmentation Datasets Deep learning Digital imaging few-shot learning graph Laplacian Machine learning Medical imaging Microorganisms
Online Access:	Get full text
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Summary:	Identification and classification of bacterial genera and species are very important for medical prevention, diagnosis, and treatment. However, due to microbial diversity and high variability in appearance, the manual classification of bacteria is a challenging and time-consuming task. This paper aims to facilitate such a troublesome task using deep learning techniques. Through the utilization of a deep learning model, specifically a Big Transfer (BiT) combined with graph Laplacian-based data cleaning and weight initialization based-rectified linear unit (WIB-Relu) activation, we have developed an accurate bacteria classification model. We have tested our proposed method on a public dataset of microscopic bacteria images, called the Digital Images of Bacteria Species (DIBaS), and achieved promising results with an accuracy of 99.11%, precision of 99.31%, recall of 99.09%, and F1 score of 99.06%, respectively. Moreover, the proposed bacteria classification performed well regardless of the size of the training data. We investigated its generalizability not only on the original dataset but also on the few shots (5-shots, 2-shots, and 1-shot) and augmented datasets.
ISSN:	2169-3536 2169-3536
DOI:	10.1109/ACCESS.2024.3358671