Classification method of fMRI data based on broad learning system

被引：2

作者：

Liu J.-C. ^{[1
]}

Ji J.-Z. ^{[1
]}

机构：

[1] Faculty of Information Technology, Beijing University of Technology, Beijing

来源：

Ji, Jun-Zhong (jjz01@bjut.edu.cn) | 1600年 / Zhejiang University卷 / 55期

关键词：

Broad learning system; Deep learning; Feature enhancement; Functional magnetic resonance imaging (fMRI) data classification; Random feature mapping; Ridge regression inverse;

D O I：

10.3785/j.issn.1008-973X.2021.07.006

中图分类号：

学科分类号：

摘要：

A functional magnetic resonance imaging (fMRI) data classification method based on broad learning system was proposed. The deep features of fMRI data were extracted through a simple structure to speed up the classification. Using the time series of the mean values of the voxel in the region of interest in fMRI the input data was constructed. The shallow and deep features of fMRI data were extracted respectively, mapped to feature nodes and enhancement nodes for broad learning, and a model framework was built. Ridge regression was used to inversely calculate the connection weights of the classification model to achieve fMRI data classification. ABIDE I, ABIDE II and ADHD-200 were used to compare the proposed method with six classification methods. Results show that the proposed method can maintain good classification accuracy while reduce training time greatly. © 2021, Zhejiang University Press. All right reserved.

引用

页码：1270 / 1278

页数：8

共 25 条

[1] BELLIVEAU J W, KENNEDY D N, MCKINSTRY R C, Et al., Functional mapping of the human visual cortex by magnetic resonance imaging, Science, 254, 5032, pp. 716-719, (1991)
[2] COX D D, SAVOY R L., Functional magnetic resonance imaging (fMRI)"brain reading": detecting and classifying distributed patterns of fMRI activity in human visual cortex, Neuro Image, 19, 2, pp. 261-270, (2003)
[3] CHENG B, LIU M, SHEN D, Et al., Multi-domain transfer learning for early diagnosis of Alzheimer ' s disease, Brain, 135, 5, pp. 1498-1507, (2012)
[4] ROSA M J, PORTUGAL L, HAHN T, Et al., Sparse network-based models for patient classification using fMRI, Neuroimage, 105, pp. 493-506, (2015)
[5] SACCHET M D, PRASAD G, FOLAND-ROSS L C, Et al., Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory, Frontiers in Psychiatry, 6, (2015)
[6] KHAZAEE A, EBRAHIMZADEH A, BABAJANI-FEREMI A., Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer ' s disease, Brain Imaging and Behavior, 10, 3, pp. 799-817, (2016)
[7] KHAZAEE A, EBRAHIMZADEH A, BABAJANI-FEREMI A., Automatic classification of Alzheimer's disease with resting-state fMRI and graph theory, 2014 21th Iranian Conference on Biomedical Engineering (ICBME), pp. 252-257, (2014)
[8] CHENG B, LIU M, SHEN D, Et al., Multi-domain transfer learning for early diagnosis of Alzheimer ' s disease, Neuroinformatics, 15, 2, pp. 115-132, (2017)
[9] LI H, XUE Z, ELLMORE T M, Et al., Identification of faulty DTI-based sub-networks in autism using network regularized SVM, 2012 9th IEEE International Symposium on Biomedical Imaging(ISBI), pp. 550-553, (2012)
[10] DODERO L, MINH H Q, SAN BIAGIO M, Et al., Kernel-based classification for brain connectivity graphs on the Riemannian manifold of positive definite matrices [C], 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp. 42-45, (2015)

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