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...
| Main Authors: | , |
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| Format: | Proceeding Paper |
| Language: | English |
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International Neuroinformatics Coordinating Facilities (INCF)
2016
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/61306/ http://irep.iium.edu.my/61306/6/61306-Machine%20learning.pdf |
| _version_ | 1848785647798583296 |
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| 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 |
| building | IIUM Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-14T16:56:28Z |
| format | Proceeding Paper |
| id | iium-61306 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T16:56:28Z |
| publishDate | 2016 |
| publisher | International Neuroinformatics Coordinating Facilities (INCF) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-613062018-06-29T03:28:37Z 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 Proceeding Paper 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 |
| spellingShingle | T Technology (General) Mohd Suhaimi, Nur Farahana Htike@Muhammad Yusof, Zaw Zaw Machine learning in fMRI classification |
| title | 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_short | Machine learning in fMRI classification |
| title_sort | machine learning in fmri classification |
| topic | T Technology (General) |
| url | http://irep.iium.edu.my/61306/ http://irep.iium.edu.my/61306/ http://irep.iium.edu.my/61306/ http://irep.iium.edu.my/61306/6/61306-Machine%20learning.pdf |