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Multi-modal MRI-Based Classification of Brain Tumors. A Comprehensive Analysis of 17 Distinct Classes

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Al-Azhar University – Gaza
Taha A.M.H.; Ariffin S.B.B.; Abu-Naser S.S.
Taha, Ashraf M. H. (58065160100); Ariffin, Syaiba Balqish Binti (55570993100); Abu-Naser, Samy S. (26533902900)
58065160100; 55570993100; 26533902900
2024
Lecture Notes on Data Engineering and Communications Technologies
Multi-modal MRI-Based Classification of Brain Tumors. A Comprehensive Analysis of 17 Distinct Classes
210
39
50
Springer Science and Business Media Deutschland GmbH
University Malaysia of Computer Science and Engineering (UNIMY), Cyberjaya, Malaysia; Department of Information Technology, Faculty of Engineering and Information Technology, Al-Azhar University, Gaza, Palestine
Taha A.M.H., University Malaysia of Computer Science and Engineering (UNIMY), Cyberjaya, Malaysia; Ariffin S.B.B., University Malaysia of Computer Science and Engineering (UNIMY), Cyberjaya, Malaysia; Abu-Naser S.S., Department of Information Technology, Faculty of Engineering and Information Technology, Al-Azhar University, Gaza, Palestine
S.S. Abu-Naser; Department of Information Technology, Faculty of Engineering and Information Technology, Al-Azhar University, Gaza, Palestine; email: abunaser@alazhar.edu.ps
1
10.1007/978-3-031-59711-4_4
Brain tumor classification; Deep learning; Medical imaging; Multi-modal MRI; Xception pre-trained model
Brain; Classification (of information); Computer aided diagnosis; Deep learning; Magnetic resonance imaging; Modal analysis; Patient treatment; Statistical tests; Tumors; Brain tumor classifications; Brain tumors; Comprehensive analysis; Critical tasks; Deep learning; Multi-modal; Multi-modal MRI; Performance; Testing sets; Xception pre-trained model; Medical imaging