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

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

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Abstract

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.

Item Type:	Article (Journal)
Uncontrolled Keywords:	Binary classification, Brain tumor, CNN, Medical images, MRI, Multiclass classification
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology
Depositing User:	Dr. Raini Hassan
Date Deposited:	07 Aug 2023 15:46
Last Modified:	07 Aug 2023 15:46
URI:	http://irep.iium.edu.my/id/eprint/105865

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