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DOI: 10.14569/IJACSA.2022.0131024
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A Systematic Literature Review of Deep Learning-Based Detection and Classification Methods for Bacterial Colonies

Author 1: Shimaa A. Nagro

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 10, 2022.

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Abstract: Deep learning is an area of machine learning that has substantial potential in various fields of study such as image processing and computer vision. A large number of studies are published annually on deep learning techniques. The focus of this paper is on bacteria detection, identification, and classification. This paper presents a systematic literature review that synthesizes the evidence related to bacteria colony identification and detection published in the year 2021. The aim is to aggregate, analyse, and summarize the evidence related to deep learning detection, identification, and classification of bacteria and bacteria colonies. The significance is that the review will help experts and technicians to understand how deep learning techniques can apply in this regard and potentially further support more accurate detection of bacteria types. A total of 38 studies are analysed. The majority of the published studies focus on supervised-learning–based convolutional neural networks. Furthermore, a large number of studies make use of laboratory-prepared datasets as compared to open-source and industrial datasets. The results also indicate a lack of tools, which is a barrier in adapting academic research in industrial settings.

Keywords: AI; bacterial-colonies; classification; deep learning; detection; literature review

Shimaa A. Nagro, “A Systematic Literature Review of Deep Learning-Based Detection and Classification Methods for Bacterial Colonies” International Journal of Advanced Computer Science and Applications(IJACSA), 13(10), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131024

@article{Nagro2022,
title = {A Systematic Literature Review of Deep Learning-Based Detection and Classification Methods for Bacterial Colonies},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131024},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131024},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {10},
author = {Shimaa A. Nagro}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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