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Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks

The accuracy of brain tumor diagnosis based on medical images is greatly affected by the segmentation process. The segmentation determines the tumor shape, location, size, and texture. In this study, we proposed a new segmentation approach for brain tissues usingMR images. The method includes thr...

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Main Authors:	Arunkumar, N., Mohammed, Mazin Abed, A. Mostafa, Salama, Ahmed Ibrahim, Dheyaa, Rodrigues, Joel J. P. C., C. de Albuquerque, Victor Hugo
Format:	Article
Language:	English
Published:	Wiley 2018
Subjects:	TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Online Access:	http://eprints.uthm.edu.my/876/1/DNJ9704_bf8346f763a86328c625a150b872810a.pdf http://eprints.uthm.edu.my/876/ https://doi.org/10.1002/cpe.4962
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spelling	my.uthm.eprints.8762021-10-17T07:07:18Z http://eprints.uthm.edu.my/876/ Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks Arunkumar, N. Mohammed, Mazin Abed A. Mostafa, Salama Ahmed Ibrahim, Dheyaa Rodrigues, Joel J. P. C. C. de Albuquerque, Victor Hugo TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television The accuracy of brain tumor diagnosis based on medical images is greatly affected by the segmentation process. The segmentation determines the tumor shape, location, size, and texture. In this study, we proposed a new segmentation approach for brain tissues usingMR images. The method includes three computer vision fiction strategieswhich are enhancing images, segmenting images, and filtering out non ROI based on the texture and HOGfeatures. A fully automatic model-based trainable segmentation and classification approach for MRI brain tumour using artificial neural networks to precisely identifying the location of the ROI. Therefore, the filtering out non ROI process have used in view of histogram investigation to avert the non ROI and select the correct object in brain MRI.However, identification the tumor kind utilizing the texture features.Atotal of 200 MRI cases are utilized for the comparing between automatic and manual segmentation procedure. The outcomes analysis shows that the fully automatic model-based trainable segmentation over performs the manual method and the brain identification utilizing the ROI texture features. The recorded identification precision is 92.14%,with 89 sensitivity and 94 specificity. Wiley 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/876/1/DNJ9704_bf8346f763a86328c625a150b872810a.pdf Arunkumar, N. and Mohammed, Mazin Abed and A. Mostafa, Salama and Ahmed Ibrahim, Dheyaa and Rodrigues, Joel J. P. C. and C. de Albuquerque, Victor Hugo (2018) Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks. Concurrency Computat Pract Exper. pp. 1-9. https://doi.org/10.1002/cpe.4962
institution	Universiti Tun Hussein Onn Malaysia
building	UTHM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tun Hussein Onn Malaysia
content_source	UTHM Institutional Repository
url_provider	http://eprints.uthm.edu.my/
language	English
topic	TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
spellingShingle	TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television Arunkumar, N. Mohammed, Mazin Abed A. Mostafa, Salama Ahmed Ibrahim, Dheyaa Rodrigues, Joel J. P. C. C. de Albuquerque, Victor Hugo Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks
description	The accuracy of brain tumor diagnosis based on medical images is greatly affected by the segmentation process. The segmentation determines the tumor shape, location, size, and texture. In this study, we proposed a new segmentation approach for brain tissues usingMR images. The method includes three computer vision fiction strategieswhich are enhancing images, segmenting images, and filtering out non ROI based on the texture and HOGfeatures. A fully automatic model-based trainable segmentation and classification approach for MRI brain tumour using artificial neural networks to precisely identifying the location of the ROI. Therefore, the filtering out non ROI process have used in view of histogram investigation to avert the non ROI and select the correct object in brain MRI.However, identification the tumor kind utilizing the texture features.Atotal of 200 MRI cases are utilized for the comparing between automatic and manual segmentation procedure. The outcomes analysis shows that the fully automatic model-based trainable segmentation over performs the manual method and the brain identification utilizing the ROI texture features. The recorded identification precision is 92.14%,with 89 sensitivity and 94 specificity.
format	Article
author	Arunkumar, N. Mohammed, Mazin Abed A. Mostafa, Salama Ahmed Ibrahim, Dheyaa Rodrigues, Joel J. P. C. C. de Albuquerque, Victor Hugo
author_facet	Arunkumar, N. Mohammed, Mazin Abed A. Mostafa, Salama Ahmed Ibrahim, Dheyaa Rodrigues, Joel J. P. C. C. de Albuquerque, Victor Hugo
author_sort	Arunkumar, N.
title	Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks
title_short	Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks
title_full	Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks
title_fullStr	Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks
title_full_unstemmed	Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks
title_sort	fully automatic model-based segmentation and classification approach for mri brain tumor using artificial neural networks
publisher	Wiley
publishDate	2018
url	http://eprints.uthm.edu.my/876/1/DNJ9704_bf8346f763a86328c625a150b872810a.pdf http://eprints.uthm.edu.my/876/ https://doi.org/10.1002/cpe.4962
_version_	1738580793811271680
score	13.211869

Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks

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