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

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...

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Main Authors: Son Ali, Akbar, Kamarul Hawari, Ghazali, Doni, Subekti, Yudhana, Anton, Liya Yusrina, Sabila, Wahyu Sapto, Aji, Habsah, Hasan
Format: Conference or Workshop Item
Language:English
English
Published: IEEE 2022
Subjects:
QH Natural history
RB Pathology
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/37878/1/Classification%20of%20gram-positive%20and%20gram-negative%20bacterial%20images%20.pdf
http://umpir.ump.edu.my/id/eprint/37878/2/Classification%20of%20gram-positive%20and%20gram-negative%20bacterial%20images%20_FULL.pdf
http://umpir.ump.edu.my/id/eprint/37878/
https://doi.org/10.1109/ICOIACT55506.2022.9971815
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spelling my.ump.umpir.378782023-06-27T03:57:03Z http://umpir.ump.edu.my/id/eprint/37878/ Classification of gram-positive and gram-negative bacterial images based on machine learning algorithm Son Ali, Akbar Kamarul Hawari, Ghazali Doni, Subekti Yudhana, Anton Liya Yusrina, Sabila Wahyu Sapto, Aji Habsah, Hasan QH Natural history RB Pathology TK Electrical engineering. Electronics Nuclear engineering 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. IEEE 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/37878/1/Classification%20of%20gram-positive%20and%20gram-negative%20bacterial%20images%20.pdf pdf en http://umpir.ump.edu.my/id/eprint/37878/2/Classification%20of%20gram-positive%20and%20gram-negative%20bacterial%20images%20_FULL.pdf 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 https://doi.org/10.1109/ICOIACT55506.2022.9971815
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic QH Natural history
RB Pathology
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle QH Natural history
RB Pathology
TK Electrical engineering. Electronics Nuclear engineering
Son Ali, Akbar
Kamarul Hawari, Ghazali
Doni, Subekti
Yudhana, Anton
Liya Yusrina, Sabila
Wahyu Sapto, Aji
Habsah, Hasan
Classification of gram-positive and gram-negative bacterial images based on machine learning algorithm
description 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.
format Conference or Workshop Item
author Son Ali, Akbar
Kamarul Hawari, Ghazali
Doni, Subekti
Yudhana, Anton
Liya Yusrina, Sabila
Wahyu Sapto, Aji
Habsah, Hasan
author_facet Son Ali, Akbar
Kamarul Hawari, Ghazali
Doni, Subekti
Yudhana, Anton
Liya Yusrina, Sabila
Wahyu Sapto, Aji
Habsah, Hasan
author_sort Son Ali, Akbar
title Classification of gram-positive and gram-negative bacterial images based on machine learning algorithm
title_short Classification of gram-positive and gram-negative bacterial images based on machine learning algorithm
title_full Classification of gram-positive and gram-negative bacterial images based on machine learning algorithm
title_fullStr Classification of gram-positive and gram-negative bacterial images based on machine learning algorithm
title_full_unstemmed Classification of gram-positive and gram-negative bacterial images based on machine learning algorithm
title_sort classification of gram-positive and gram-negative bacterial images based on machine learning algorithm
publisher IEEE
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/37878/1/Classification%20of%20gram-positive%20and%20gram-negative%20bacterial%20images%20.pdf
http://umpir.ump.edu.my/id/eprint/37878/2/Classification%20of%20gram-positive%20and%20gram-negative%20bacterial%20images%20_FULL.pdf
http://umpir.ump.edu.my/id/eprint/37878/
https://doi.org/10.1109/ICOIACT55506.2022.9971815
_version_ 1769842533957894144
score 13.211869

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