Abstract

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Title:: Diagnosis of youth depression based on transfer learning and 3D-WGMobileNet

Author(s):: GUO Zhaohui; WANG Yu; MA Huijun; TIAN Hengyi; School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China

Keywords:: Keywords: depression 3D-WGMobileNet dynamic grouping convolution sliding window grouping convolution transfer learning functional magnetic resonance imaging

Abstract:: A novel network model is proposed based on 3D-WGMobileNet and transfer learning to accurately diagnose the stage of youth depression. After functional magenetic resonance imaging (fMRI) data preprocessing and dimensionality reduction by converting 4-dimensional fMRI data into 3-dimension through regional homogeneity approach, transfer learning method is employed to transfer the characteristics of Alzheimers disease to the proposed 3D-WGMobileNet. The expert weight matrix of convolutional kernel is constructed using dynamic grouping convolution for improving the expression ability of the model. The sliding window grouping convolution is used to reduce the quantity of model parameters and enhance the computing capability. Finally, 3D-WGMobileNet is used for the image feature extraction and classification of youth depression. Experimental results on the dataset of human connectome projects show that the 3D-WGMobileNet incorporating transfer learning, dynamic grouping convolution and sliding window grouping convolution exhibit superior performance in classification, achieving 89.00%, 85.15% and 87.90% accuracies in classifying depression and healthy controls, mild depression and healthy controls, mild depression and moderate depression, which verifies the feasibility and effectiveness of the proposed method.