Enhancement of tuberculosis detection using ensemble classifier with Quadtree method: A preliminary study

Tuberculosis is an infectious disease caused by a bacillus called Mycobacterium tuberculosis. It can lead to death in untreated and inappropriately treated patients. An early diagnosis of the disease not only improves treatment success but also reduces death rates. Lung region is the most affected p...

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Main Authors: Chong, Joon Hou, Jack, Laura P., Baharum, Aslina, Mat Noor, Noorsidi Aizuddin
Format: Article
Published: Avanti Publishers 2020
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Online Access:http://eprints.utm.my/id/eprint/90170/
http://dx.doi.org/10.31875/2409-9848.2020.07.1
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spelling my.utm.901702021-03-31T06:21:56Z http://eprints.utm.my/id/eprint/90170/ Enhancement of tuberculosis detection using ensemble classifier with Quadtree method: A preliminary study Chong, Joon Hou Jack, Laura P. Baharum, Aslina Mat Noor, Noorsidi Aizuddin Q Science (General) Tuberculosis is an infectious disease caused by a bacillus called Mycobacterium tuberculosis. It can lead to death in untreated and inappropriately treated patients. An early diagnosis of the disease not only improves treatment success but also reduces death rates. Lung region is the most affected part of Tuberculosis and the process of medical image classification is still carried out manually using the knowledge of the physician or radiologist, which leads to inaccurate and slow process of TB identification. Therefore, this study proposed to enhance tuberculosis detection using a different combination of machine learning and image processing methods on the image dataset. Avanti Publishers 2020 Article PeerReviewed Chong, Joon Hou and Jack, Laura P. and Baharum, Aslina and Mat Noor, Noorsidi Aizuddin (2020) Enhancement of tuberculosis detection using ensemble classifier with Quadtree method: A preliminary study. Journal Of Modern Mechanical Engineering and Technology, 7 . p. 6. ISSN 2409-9848 http://dx.doi.org/10.31875/2409-9848.2020.07.1
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic Q Science (General)
spellingShingle Q Science (General)
Chong, Joon Hou
Jack, Laura P.
Baharum, Aslina
Mat Noor, Noorsidi Aizuddin
Enhancement of tuberculosis detection using ensemble classifier with Quadtree method: A preliminary study
description Tuberculosis is an infectious disease caused by a bacillus called Mycobacterium tuberculosis. It can lead to death in untreated and inappropriately treated patients. An early diagnosis of the disease not only improves treatment success but also reduces death rates. Lung region is the most affected part of Tuberculosis and the process of medical image classification is still carried out manually using the knowledge of the physician or radiologist, which leads to inaccurate and slow process of TB identification. Therefore, this study proposed to enhance tuberculosis detection using a different combination of machine learning and image processing methods on the image dataset.
format Article
author Chong, Joon Hou
Jack, Laura P.
Baharum, Aslina
Mat Noor, Noorsidi Aizuddin
author_facet Chong, Joon Hou
Jack, Laura P.
Baharum, Aslina
Mat Noor, Noorsidi Aizuddin
author_sort Chong, Joon Hou
title Enhancement of tuberculosis detection using ensemble classifier with Quadtree method: A preliminary study
title_short Enhancement of tuberculosis detection using ensemble classifier with Quadtree method: A preliminary study
title_full Enhancement of tuberculosis detection using ensemble classifier with Quadtree method: A preliminary study
title_fullStr Enhancement of tuberculosis detection using ensemble classifier with Quadtree method: A preliminary study
title_full_unstemmed Enhancement of tuberculosis detection using ensemble classifier with Quadtree method: A preliminary study
title_sort enhancement of tuberculosis detection using ensemble classifier with quadtree method: a preliminary study
publisher Avanti Publishers
publishDate 2020
url http://eprints.utm.my/id/eprint/90170/
http://dx.doi.org/10.31875/2409-9848.2020.07.1
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score 13.160551