Multi-View Modeling Method for Functional MRI Images
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Published:2021-02-01
Issue:2
Volume:11
Page:432-436
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ISSN:2156-7018
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Container-title:Journal of Medical Imaging and Health Informatics
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language:en
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Short-container-title:j med imaging hlth inform
Author:
Zhu Jinlong,Hu Xiujian,Zhang Chao,Sheng Guanglei
Abstract
This paper proposes a new unsupervised fuzzy feature mapping method based on fMRI data and combines it with multi-view support vector machine to construct a classification model for computer-aided diagnosis of autism. Firstly, a multi-output TSK fuzzy system is adopted to map the original
feature data to the linear separable high-dimensional space. Then a manifold regularization learning framework is introduced, and a new method of unsupervised fuzzy feature learning is proposed. Finally, a multi-view SVM algorithm is used for classification tasks. The experimental results
show that the method in this paper can effectively extract important features from the fMRI data in the resting state and improve the model's interpretability on the premise of ensuring the superior and stable classification performance of the model.
Publisher
American Scientific Publishers
Subject
Health Informatics,Radiology Nuclear Medicine and imaging
Cited by
1 articles.
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