Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/82016
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dc.contributorDepartment of Chinese and Bilingual Studiesen_US
dc.creatorHu, Jen_US
dc.creatorKuang, Yen_US
dc.creatorLiao, Ben_US
dc.creatorCao, Len_US
dc.creatorDong, Sen_US
dc.creatorLi, Pen_US
dc.date.accessioned2020-05-05T05:58:18Z-
dc.date.available2020-05-05T05:58:18Z-
dc.identifier.issn1687-5265en_US
dc.identifier.urihttp://hdl.handle.net/10397/82016-
dc.language.isoenen_US
dc.publisherHindawi Ltden_US
dc.rightsCopyright © 2019 Jinlong Hu et al. This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.rightsThe following publication Hu, J. L., Kuang, Y. Z., Liao, B., Cao, L. J., Dong, S. B., & Li, P. (2019). A multichannel 2D convolutional neural network model for task-evoked fMRI data classification. Computational Intelligence and Neuroscience, 5065214, 1-9 is available at https://dx.doi.org/10.1155/2019/5065214en_US
dc.titleA multichannel 2D convolutional neural network model for task-evoked fMRI data classificationen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1-
dc.identifier.epage9-
dc.identifier.volume2019en_US
dc.identifier.doi10.1155/2019/5065214en_US
dcterms.abstractDeep learning models have been successfully applied to the analysis of various functional MRI data. Convolutional neural networks (CNN), a class of deep neural networks, have been found to excel at extracting local meaningful features based on their shared-weights architecture and space invariance characteristics. In this study, we propose M2D CNN, a novel multichannel 2D CNN model, to classify 3D fMRI data. The model uses sliced 2D fMRI data as input and integrates multichannel information learned from 2D CNN networks. We experimentally compared the proposed M2D CNN against several widely used models including SVM, 1D CNN, 2D CNN, 3D CNN, and 3D separable CNN with respect to their performance in classifying task-based fMRI data. We tested M2D CNN against six models as benchmarks to classify a large number of time-series whole-brain imaging data based on a motor task in the Human Connectome Project (HCP). The results of our experiments demonstrate the following: (i) convolution operations in the CNN models are advantageous for high-dimensional whole-brain imaging data classification, as all CNN models outperform SVM; (ii) 3D CNN models achieve higher accuracy than 2D CNN and 1D CNN model, but 3D CNN models are computationally costly as any extra dimension is added in the input; (iii) the M2D CNN model proposed in this study achieves the highest accuracy and alleviates data overfitting given its smaller number of parameters as compared with 3D CNN.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationComputational intelligence and neuroscience, 31 Dec. 2019, v. 2019, 5065214, p. 1-9en_US
dcterms.isPartOfComputational Intelligence and Neuroscienceen_US
dcterms.issued2019-
dc.identifier.isiWOS:000507293400001-
dc.identifier.scopus2-s2.0-85077943825-
dc.identifier.eissn1687-5273en_US
dc.identifier.artn5065214en_US
dc.description.validate202006 bcmaen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumbera0435-n08en_US
dc.description.pubStatusPublisheden_US
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