Multi-View Modeling Method for Functional MRI Images
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.
Keywords: AUTISM; MANIFOLD REGULARIZATION FRAMEWORK; SUPPORT VECTOR MACHINE; UNSUPERVISED FUZZY FEATURE MAPPING
Document Type: Research Article
Publication date: 01 February 2021
- Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
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