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Feature map size selection for fMRI classification on end-to-end deep convolutional neural networks
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Feature map size selection for fMRI classification on end-to-end deep convolutional neural networks

The emergence of convolutional neural networks (CNN) in various fields has also paved numerous ways for advancement in the field of medical imaging. This paper focuses on functional magnetic resonance imaging (fMRI) in the field of neuroimaging. It has high temporal resolution and robust to control...

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Bibliographic Details
Main Authors: Suhaimi, Farahana, Htike@Muhammad Yusof, Zaw Zaw
Format: Article
Language:English
English
Published: Science Gate 2018
Subjects:
Q350 Information theory
Online Access:http://irep.iium.edu.my/69669/
http://irep.iium.edu.my/69669/13/69669%20Feature%20map%20size%20selection%20for%20fMRI%20classification%20on%20end-to-end%20deep%20convolutional%20neural%20networks_wos.pdf
http://irep.iium.edu.my/69669/19/69669_Feature%20map%20size%20selection%20for%20fMRI%20classification%20on%20end-to-end%20deep%20convolutional%20neural%20networks_ARTICLE.pdf
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http://irep.iium.edu.my/69669/
http://irep.iium.edu.my/69669/13/69669%20Feature%20map%20size%20selection%20for%20fMRI%20classification%20on%20end-to-end%20deep%20convolutional%20neural%20networks_wos.pdf
http://irep.iium.edu.my/69669/19/69669_Feature%20map%20size%20selection%20for%20fMRI%20classification%20on%20end-to-end%20deep%20convolutional%20neural%20networks_ARTICLE.pdf

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