Youth Depression Diagnosis Algorithm Based on 3D-WGMobileNet and Transfer Learning

Depression is a common mental illness that not only profoundly infests the psychological state of patients, but also tends to cause damage to the functioning of patients' brain areas.To construct a comprehensive and detailed framework for a supporting diagnostic network that will help physicians make accurate and timely diagnoses when dealing with patients at different stages of depression, a network model based on three-dimensional (3D) weight group MobileNet (3D-WGMobileNet) and transfer learning is proposed.Firstly, fMRI data is preprocessed, and regional homogeneity analysis is used to reduce the dimension of the image.Then, the characteristics of Alzheimer's disease are learned by transfer learning and transferred to the proposed model.Next, the dynamic group convolution was used to construct the expert weight matrix of the convolution kernel, and the sliding window group convolution was used to compress the parameters of the model to improve the expression ability and computing power of the model.By using 5-fold cross-validation, we conducted experiments using data from HCP and RESTmeta-MDD.The experiment results show that the proposed model gives a superior performance compared with other state-of-the-art methods, especially on the classification of the healthy group with major depression groups, where the two datasets achieve 88% and 91% accuracy, respectively, which verifies the feasibility and effectiveness of our model.