Classification Method of fMRI Data Based on Convolutional Neural Network
ZHANG Zhaochen, JI Junzhong
Multimedia and Intelligent Software Technology, Beijing Municipal Key Laboratory, College of Computer Science, Beijing University of Technology, Beijing 100124
Abstract Since classification method of functional magnetic resonance imaging(fMRI) data can not effectively extract the local features, the classification accuracy is seriously affected. To solve the problem, a classification model of fMRI data based on convolutional neural network(CNN) is presented. Firstly, a CNN structure is designed, and a restricted boltzmann machine(RBM) model is constructed by means of the convolution kernel size. Then, the interested region voxels in fMRI data are employed to construct and form input data to pre-train RBM, and the relative transformation of the obtained weight matrix is executed to initialize CNN parameters. Finally, the final classification model is obtained by training the whole initialized model. The results on Haxby and LPD datasets show that the proposed model effectively improves the classification accuracy of fMRI data.
Fund:Supported by National Natural Science Foundation of China(No.61672065,61375059)
About author:: (ZHANG Zhaochen, born in 1992, master student. His research interests include machine learning and biological information mining.) (JI Junzhong(Corresponding author), born in 1969, Ph.D., professor. His research interests include data mining, machine learning, swarm intelligence and bioinformatics.)
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2017, Vol. 30 
