Multi-level of feature extraction and classification for X-Ray medical image

There has been a rise in demand for digitized medical images over the last two decades. Medical images' pivotal role in surgical planning is also an essential source of information for diseases and as medical reference as well as for the purpose of research and training....

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Main Authors:	Abdulrazaq, M, Alshaikhli, Imad Fakhri Taha, Mohd Noah, Shahrul Azman, Fadhil,, Moayad Al Athami
Format:	Article
Language:	English English
Published:	Institute of Advanced Engineering and Science (IAES) 2018
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://irep.iium.edu.my/63248/ http://irep.iium.edu.my/63248/ http://irep.iium.edu.my/63248/ http://irep.iium.edu.my/63248/13/63248%20Multi-Level%20of%20Feature%20Extraction%20and%20Classification%20for%20X-Ray%20%20SCOPUS.pdf http://irep.iium.edu.my/63248/19/63248_Multi-Level%20of%20Feature%20Extraction%20and%20Classification%20for%20X-Ray_article.pdf

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recordtype	eprints
spelling	iium-632482018-08-10T03:32:08Z http://irep.iium.edu.my/63248/ Multi-level of feature extraction and classification for X-Ray medical image Abdulrazaq, M Alshaikhli, Imad Fakhri Taha Mohd Noah, Shahrul Azman Fadhil,, Moayad Al Athami QA75 Electronic computers. Computer science There has been a rise in demand for digitized medical images over the last two decades. Medical images' pivotal role in surgical planning is also an essential source of information for diseases and as medical reference as well as for the purpose of research and training. Therefore, effective techniques for medical image retrieval and classification are required to provide accurate search through substantial amount of images in a timely manner. Given the amount of images that are required to deal with, it is a non-viable practice to manually annotate these medical images. Additionally, retrieving and indexing them with image visual feature cannot capture high level of semantic concepts, which are necessary for accurate retrieval and effective classification of medical images. Therefore, an automatic mechanism is required to address these limitations. Addressing this, this study formulated an effective classification for X-ray medical images using different feature extractions and classification techniques. Specifically, this study proposed pertinent feature extraction algorithm for X-ray medical images and determined machine learning methods for automatic X-ray medical image classification. This study also evaluated different image features (chiefly global, local, and combined) and classifiers. Consequently, the obtained results from this study improved results obtained from previous related studies. Institute of Advanced Engineering and Science (IAES) 2018-04-01 Article PeerReviewed application/pdf en http://irep.iium.edu.my/63248/13/63248%20Multi-Level%20of%20Feature%20Extraction%20and%20Classification%20for%20X-Ray%20%20SCOPUS.pdf application/pdf en http://irep.iium.edu.my/63248/19/63248_Multi-Level%20of%20Feature%20Extraction%20and%20Classification%20for%20X-Ray_article.pdf Abdulrazaq, M and Alshaikhli, Imad Fakhri Taha and Mohd Noah, Shahrul Azman and Fadhil,, Moayad Al Athami (2018) Multi-level of feature extraction and classification for X-Ray medical image. Indonesian Journal of Electrical Engineering and Computer Science, 10 (1). pp. 154-167. ISSN 2502-4752 http://www.iaescore.com/journals/index.php/IJEECS/article/view/11066/8196 10.11591/ijeecs.v10.i1.pp154-167
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Abdulrazaq, M Alshaikhli, Imad Fakhri Taha Mohd Noah, Shahrul Azman Fadhil,, Moayad Al Athami Multi-level of feature extraction and classification for X-Ray medical image
description	There has been a rise in demand for digitized medical images over the last two decades. Medical images' pivotal role in surgical planning is also an essential source of information for diseases and as medical reference as well as for the purpose of research and training. Therefore, effective techniques for medical image retrieval and classification are required to provide accurate search through substantial amount of images in a timely manner. Given the amount of images that are required to deal with, it is a non-viable practice to manually annotate these medical images. Additionally, retrieving and indexing them with image visual feature cannot capture high level of semantic concepts, which are necessary for accurate retrieval and effective classification of medical images. Therefore, an automatic mechanism is required to address these limitations. Addressing this, this study formulated an effective classification for X-ray medical images using different feature extractions and classification techniques. Specifically, this study proposed pertinent feature extraction algorithm for X-ray medical images and determined machine learning methods for automatic X-ray medical image classification. This study also evaluated different image features (chiefly global, local, and combined) and classifiers. Consequently, the obtained results from this study improved results obtained from previous related studies.
format	Article
author	Abdulrazaq, M Alshaikhli, Imad Fakhri Taha Mohd Noah, Shahrul Azman Fadhil,, Moayad Al Athami
author_facet	Abdulrazaq, M Alshaikhli, Imad Fakhri Taha Mohd Noah, Shahrul Azman Fadhil,, Moayad Al Athami
author_sort	Abdulrazaq, M
title	Multi-level of feature extraction and classification for X-Ray medical image
title_short	Multi-level of feature extraction and classification for X-Ray medical image
title_full	Multi-level of feature extraction and classification for X-Ray medical image
title_fullStr	Multi-level of feature extraction and classification for X-Ray medical image
title_full_unstemmed	Multi-level of feature extraction and classification for X-Ray medical image
title_sort	multi-level of feature extraction and classification for x-ray medical image
publisher	Institute of Advanced Engineering and Science (IAES)
publishDate	2018
url	http://irep.iium.edu.my/63248/ http://irep.iium.edu.my/63248/ http://irep.iium.edu.my/63248/ http://irep.iium.edu.my/63248/13/63248%20Multi-Level%20of%20Feature%20Extraction%20and%20Classification%20for%20X-Ray%20%20SCOPUS.pdf http://irep.iium.edu.my/63248/19/63248_Multi-Level%20of%20Feature%20Extraction%20and%20Classification%20for%20X-Ray_article.pdf
first_indexed	2023-09-18T21:29:42Z
last_indexed	2023-09-18T21:29:42Z
_version_	1777412428559024128

Multi-level of feature extraction and classification for X-Ray medical image

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