Brain tumor MRI medical images classification model based on CNN (BTMIC-CNN)

This research discusses a fully automatic brain tumour MRI medical images classification model that use Convolutional Neural Network (BTMIC-CNN). The proposed neural model adopted Design Science Research Methodology (DSRM) to classify MRI medical images from two datasets. One for binary classificati...

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Main Authors:	Al-Galal, Sabaa Ahmed Yahya, Alshaikhli, Imad Fakhri Taha, Abdulrazzaq, M. M., Hassan, Raini
Format:	Article
Language:	English English
Published:	Taylor’s University 2022
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://irep.iium.edu.my/105865/1/105865_Brain%20tumor%20MRI.pdf http://irep.iium.edu.my/105865/7/105865_Brain%20tumor%20MRI_SCOPUS.pdf http://irep.iium.edu.my/105865/ https://jestec.taylors.edu.my/V17Issue6.htm
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Institution:	Universiti Islam Antarabangsa Malaysia
Language:	English English

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spelling	my.iium.irep.1058652023-08-07T07:46:09Z http://irep.iium.edu.my/105865/ Brain tumor MRI medical images classification model based on CNN (BTMIC-CNN) Al-Galal, Sabaa Ahmed Yahya Alshaikhli, Imad Fakhri Taha Abdulrazzaq, M. M. Hassan, Raini QA75 Electronic computers. Computer science This research discusses a fully automatic brain tumour MRI medical images classification model that use Convolutional Neural Network (BTMIC-CNN). The proposed neural model adopted Design Science Research Methodology (DSRM) to classify MRI medical images from two datasets. One for binary classification task (contains tumorous and non-tumorous images). And the second for multiclass classification task (contains three types of brain tumor MRI medical images namely: Glioma, meningioma, and pituitary). The model's excellent performance was confirmed using the evaluation metrics and reported an overall accuracy of 99%. It outperforms existing methods in terms of classification accuracy and is expected to help radiologists and doctors accurately classify brain tumours’ images. This study contributes to goal three of the Sustainable Development Goals (SDGs), which involves excellent health and well-being. Taylor’s University 2022-12 Article PeerReviewed application/pdf en http://irep.iium.edu.my/105865/1/105865_Brain%20tumor%20MRI.pdf application/pdf en http://irep.iium.edu.my/105865/7/105865_Brain%20tumor%20MRI_SCOPUS.pdf Al-Galal, Sabaa Ahmed Yahya and Alshaikhli, Imad Fakhri Taha and Abdulrazzaq, M. M. and Hassan, Raini (2022) Brain tumor MRI medical images classification model based on CNN (BTMIC-CNN). Journal of Engineering Science and Technology, 17 (6). pp. 4410-4432. ISSN 1823-4690 https://jestec.taylors.edu.my/V17Issue6.htm
institution	Universiti Islam Antarabangsa Malaysia
building	IIUM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	International Islamic University Malaysia
content_source	IIUM Repository (IREP)
url_provider	http://irep.iium.edu.my/
language	English English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Al-Galal, Sabaa Ahmed Yahya Alshaikhli, Imad Fakhri Taha Abdulrazzaq, M. M. Hassan, Raini Brain tumor MRI medical images classification model based on CNN (BTMIC-CNN)
description	This research discusses a fully automatic brain tumour MRI medical images classification model that use Convolutional Neural Network (BTMIC-CNN). The proposed neural model adopted Design Science Research Methodology (DSRM) to classify MRI medical images from two datasets. One for binary classification task (contains tumorous and non-tumorous images). And the second for multiclass classification task (contains three types of brain tumor MRI medical images namely: Glioma, meningioma, and pituitary). The model's excellent performance was confirmed using the evaluation metrics and reported an overall accuracy of 99%. It outperforms existing methods in terms of classification accuracy and is expected to help radiologists and doctors accurately classify brain tumours’ images. This study contributes to goal three of the Sustainable Development Goals (SDGs), which involves excellent health and well-being.
format	Article
author	Al-Galal, Sabaa Ahmed Yahya Alshaikhli, Imad Fakhri Taha Abdulrazzaq, M. M. Hassan, Raini
author_facet	Al-Galal, Sabaa Ahmed Yahya Alshaikhli, Imad Fakhri Taha Abdulrazzaq, M. M. Hassan, Raini
author_sort	Al-Galal, Sabaa Ahmed Yahya
title	Brain tumor MRI medical images classification model based on CNN (BTMIC-CNN)
title_short	Brain tumor MRI medical images classification model based on CNN (BTMIC-CNN)
title_full	Brain tumor MRI medical images classification model based on CNN (BTMIC-CNN)
title_fullStr	Brain tumor MRI medical images classification model based on CNN (BTMIC-CNN)
title_full_unstemmed	Brain tumor MRI medical images classification model based on CNN (BTMIC-CNN)
title_sort	brain tumor mri medical images classification model based on cnn (btmic-cnn)
publisher	Taylor’s University
publishDate	2022
url	http://irep.iium.edu.my/105865/1/105865_Brain%20tumor%20MRI.pdf http://irep.iium.edu.my/105865/7/105865_Brain%20tumor%20MRI_SCOPUS.pdf http://irep.iium.edu.my/105865/ https://jestec.taylors.edu.my/V17Issue6.htm
_version_	1775621711301967872

Brain tumor MRI medical images classification model based on CNN (BTMIC-CNN)

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