Machine learning in fMRI classification

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 auto...

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Main Authors: Mohd Suhaimi, Nur Farahana, Htike@Muhammad Yusof, Zaw Zaw
Format: Conference or Workshop Item
Language:English
Published: International Neuroinformatics Coordinating Facilities (INCF) 2016
Subjects:
Online Access:http://irep.iium.edu.my/61306/6/61306-Machine%20learning.pdf
http://irep.iium.edu.my/61306/
https://www.frontiersin.org/books/Neuroinformatics_2016/976
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Institution: Universiti Islam Antarabangsa Malaysia
Language: English
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spelling my.iium.irep.61306 http://irep.iium.edu.my/61306/ Machine learning in fMRI classification Mohd Suhaimi, Nur Farahana Htike@Muhammad Yusof, Zaw Zaw T Technology (General) 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. International Neuroinformatics Coordinating Facilities (INCF) 2016 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/61306/6/61306-Machine%20learning.pdf 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. https://www.frontiersin.org/books/Neuroinformatics_2016/976 10.3389/978-2-88919-953-2
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
topic T Technology (General)
spellingShingle T Technology (General)
Mohd Suhaimi, Nur Farahana
Htike@Muhammad Yusof, Zaw Zaw
Machine learning in fMRI classification
description 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.
format Conference or Workshop Item
author Mohd Suhaimi, Nur Farahana
Htike@Muhammad Yusof, Zaw Zaw
author_facet Mohd Suhaimi, Nur Farahana
Htike@Muhammad Yusof, Zaw Zaw
author_sort Mohd Suhaimi, Nur Farahana
title Machine learning in fMRI classification
title_short Machine learning in fMRI classification
title_full Machine learning in fMRI classification
title_fullStr Machine learning in fMRI classification
title_full_unstemmed Machine learning in fMRI classification
title_sort machine learning in fmri classification
publisher International Neuroinformatics Coordinating Facilities (INCF)
publishDate 2016
url http://irep.iium.edu.my/61306/6/61306-Machine%20learning.pdf
http://irep.iium.edu.my/61306/
https://www.frontiersin.org/books/Neuroinformatics_2016/976
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