基于卷积神经网络的fMRI数据分类方法

摘要

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.%功能性磁共振成像(fMRI)数据分类方法无法有效提取fMRI数据的局部特征,影响分类准确性.因此文中提出基于卷积神经网络的fMRI数据分类方法.首先设计卷积神经网络结构,并根据卷积神经网络的卷积核尺寸构建受限玻尔兹曼机模型.然后使用fMRI数据感兴趣区域体素构造数据,对受限玻尔兹曼机进行预训练,并将训练得到的权重矩阵进行相对变换,用于初始化卷积神经网络的卷积核参数.最后训练初始化好的整个模型,得到最终的分类模型.在Haxby和LPD数据集上的实验表明,文中方法可以有效提升fMRI数据的分类准确率.