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Machine learning in fMRI classification

Mohd Suhaimi, Nur Farahana and Htike@Muhammad Yusof, Zaw Zaw (2016) Machine learning in fMRI classification. In: Neuroinformatics 2016, 3rd-4th September 2016, Reading, United Kingdom.

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Abstract

Statistical analysis method is utilitarian in neuroimaging. For instance, SPM12, FSL and BrainVoyager are widely used for testing the hypotheses about functional magnetic resonance imaging (fMRI). However, that testing and studying of brain images mostly consist of experts work. It is not fully automatic and time-consuming. There are fractions of decision making processes by the experts that require extensive knowledge and sets of rule of thumb. Systematically, machine learning is expected to automate the process while running the embedded sets of rule of thumb during the process. In addition, pattern recognition is one of the method in machine learning that differ to working principle of SPM12 and its counterpart. The recognizing of patterns in brain images is expected to pragmatically tackle the work of testing the fMRI hypotheses. Thus, the aim of this paper is to prove the need of machine learning in fMRI classification.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: 6919/61306
Uncontrolled Keywords: FMRI, Deep Learning, Classification and Neural Networ
Subjects: T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes: Kulliyyah of Engineering
Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User: Dr. Zaw Zaw Htike
Date Deposited: 06 Feb 2018 11:53
Last Modified: 29 Jun 2018 11:28
URI: http://irep.iium.edu.my/id/eprint/61306

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