Bacteria identification from microscopic morphology using naïve bayes

Great knowledge and experience on microbiology are required for accurate bacteria identification. Automation of bacteria identification is required because there might be a shortage of skilled microbiologists and clinicians at a time of great need. We propose an automatic bacteria identification fr...

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Main Authors: Mohamad, Noor Amaleena, Jusoh Awang, Noorain, Htike@Muhammad Yusof, Zaw Zaw, Shoon , Lei Win
Format: Article
Language:English
Published: AIRCC Publishing Corporation 2014
Subjects:
Online Access:http://irep.iium.edu.my/37781/
http://irep.iium.edu.my/37781/
http://irep.iium.edu.my/37781/
http://irep.iium.edu.my/37781/1/4214ijcseit01.pdf
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recordtype eprints
spelling iium-377812018-06-20T01:12:52Z http://irep.iium.edu.my/37781/ Bacteria identification from microscopic morphology using naïve bayes Mohamad, Noor Amaleena Jusoh Awang, Noorain Htike@Muhammad Yusof, Zaw Zaw Shoon , Lei Win Q Science (General) Great knowledge and experience on microbiology are required for accurate bacteria identification. Automation of bacteria identification is required because there might be a shortage of skilled microbiologists and clinicians at a time of great need. We propose an automatic bacteria identification framework that can classify three famous classes of bacteria namely Cocci, Bacilli and Vibrio from microscopic morphology using the Naïve Bayes classifier. The proposed bacteria identification framework comprises two steps. In the first step, the system is trained using a set of microscopic images containing Cocci, Bacilli, and Vibrio. The input images are normalized to emphasize the diameter and shape features. Edge-based descriptors are then extracted from the input images. In the second step, we use the Naïve Bayes classifier to perform probabilistic inference based on the input descriptors. 64 images for each class of bacteria were used as the training set and 222 images consisting of the three classes of bacteria and other random images such as humans and airplanes were used as the test set. There are no images overlapped between the training set and the test set. The system was found to be able to accurately discriminate the three classes of bacteria. Moreover, the system was also found to be able to reject images that did not belong to any of the three classes of bacteria. The preliminary results demonstrate how a simple machine learning classifier with a set of simple image-based features can result in high classification accuracy. The preliminary results also demonstrate the efficacy and efficiency of our two-step automatic bacteria identification approach and motivate us to extend this framework to identify a variety of other types of bacteria. AIRCC Publishing Corporation 2014-04 Article PeerReviewed application/pdf en http://irep.iium.edu.my/37781/1/4214ijcseit01.pdf Mohamad, Noor Amaleena and Jusoh Awang, Noorain and Htike@Muhammad Yusof, Zaw Zaw and Shoon , Lei Win (2014) Bacteria identification from microscopic morphology using naïve bayes. International Journal of Computer Science, Engineering and Information Technology (IJCSEIT), 4 (2). pp. 1-9. ISSN 2231-3605 (P) 2231-3117 (O) http://airccse.org/journal/ijcseit/current2014.html 10.5121/ijcseit.2014.4201
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
topic Q Science (General)
spellingShingle Q Science (General)
Mohamad, Noor Amaleena
Jusoh Awang, Noorain
Htike@Muhammad Yusof, Zaw Zaw
Shoon , Lei Win
Bacteria identification from microscopic morphology using naïve bayes
description Great knowledge and experience on microbiology are required for accurate bacteria identification. Automation of bacteria identification is required because there might be a shortage of skilled microbiologists and clinicians at a time of great need. We propose an automatic bacteria identification framework that can classify three famous classes of bacteria namely Cocci, Bacilli and Vibrio from microscopic morphology using the Naïve Bayes classifier. The proposed bacteria identification framework comprises two steps. In the first step, the system is trained using a set of microscopic images containing Cocci, Bacilli, and Vibrio. The input images are normalized to emphasize the diameter and shape features. Edge-based descriptors are then extracted from the input images. In the second step, we use the Naïve Bayes classifier to perform probabilistic inference based on the input descriptors. 64 images for each class of bacteria were used as the training set and 222 images consisting of the three classes of bacteria and other random images such as humans and airplanes were used as the test set. There are no images overlapped between the training set and the test set. The system was found to be able to accurately discriminate the three classes of bacteria. Moreover, the system was also found to be able to reject images that did not belong to any of the three classes of bacteria. The preliminary results demonstrate how a simple machine learning classifier with a set of simple image-based features can result in high classification accuracy. The preliminary results also demonstrate the efficacy and efficiency of our two-step automatic bacteria identification approach and motivate us to extend this framework to identify a variety of other types of bacteria.
format Article
author Mohamad, Noor Amaleena
Jusoh Awang, Noorain
Htike@Muhammad Yusof, Zaw Zaw
Shoon , Lei Win
author_facet Mohamad, Noor Amaleena
Jusoh Awang, Noorain
Htike@Muhammad Yusof, Zaw Zaw
Shoon , Lei Win
author_sort Mohamad, Noor Amaleena
title Bacteria identification from microscopic morphology using naïve bayes
title_short Bacteria identification from microscopic morphology using naïve bayes
title_full Bacteria identification from microscopic morphology using naïve bayes
title_fullStr Bacteria identification from microscopic morphology using naïve bayes
title_full_unstemmed Bacteria identification from microscopic morphology using naïve bayes
title_sort bacteria identification from microscopic morphology using naïve bayes
publisher AIRCC Publishing Corporation
publishDate 2014
url http://irep.iium.edu.my/37781/
http://irep.iium.edu.my/37781/
http://irep.iium.edu.my/37781/
http://irep.iium.edu.my/37781/1/4214ijcseit01.pdf
first_indexed 2023-09-18T20:54:10Z
last_indexed 2023-09-18T20:54:10Z
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