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...
| Main Authors: | , , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IEEE
2022
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/37878/ 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 |
| _version_ | 1848825369700859904 |
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| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T03:27:50Z |
| format | Conference or Workshop Item |
| id | ump-37878 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:27:50Z |
| publishDate | 2022 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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 (Published) https://doi.org/10.1109/ICOIACT55506.2022.9971815 |
| 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 |
| title | 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_short | 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 |
| topic | QH Natural history RB Pathology TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/37878/ http://umpir.ump.edu.my/id/eprint/37878/ 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 |