Classification of gram-positive and gram-negative bacterial images based on machine learning algorithm

Son Ali, Akbar and Kamarul Hawari, Ghazali and Doni, Subekti and Yudhana, Anton and Liya Yusrina, Sabila and Wahyu Sapto, Aji and Habsah, Hasan (2022) Classification of gram-positive and gram-negative bacterial images based on machine learning algorithm. In: 5th International Conference on Information and Communications Technology, ICOIACT 2022 , 24 - 25 August 2022 , Yogyakarta, Indonesia. 509 -512.. ISSN 2770-4661 ISBN 978-166545140-6

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Abstract

Bacteria are small living things that cannot be seen directly, and bacteria are the main cause of various diseases, so a tool is needed that can detect them. In fact, the manual classification process necessitates a significant amount of time. In addition, the traditional diagnosis has a limitation on accurate detection. Identifying and classifying bacteria is critical for assisting the medical field. Therefore, this study aims to utilize the machine learning approach's computerized technique proposed. The method provided features extraction and classification. This research used gram-positive and gram-negative bacterial species. Two texture features are used to extract characteristics of each bacterial class: the histogram feature and the Gray Level Co-occurrence Matrix (GLCM). In addition, the Naive Bayes classifier was utilized to classify the features extracted. The final classification accuracy result is 77.5% using the histogram feature and 72% using GLCM features. Therefore, this approach might be possible to assist the clinician and microbiologist.

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Bacterial; GLCM feature; Histogram feature; Machine learning; Naive Bayes
Subjects:	Q Science > QH Natural history R Medicine > RB Pathology T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division:	Institute of Postgraduate Studies Faculty of Electrical and Electronic Engineering Technology
Depositing User:	Mrs Norsaini Abdul Samat
Date Deposited:	27 Jun 2023 03:57
Last Modified:	27 Jun 2023 03:57
URI:	http://umpir.ump.edu.my/id/eprint/37878
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