Abstract

Info

Title:: Aided diagnosis of major depressive disorder based on canonical correlation analysis and bimodal data fusion

Author(s):: JI Yarong; WANG Yu; FU Changyang; XIAO Hongbing; XING Suxia; School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China

Keywords:: Keywords: major depressive disorder functional magnetic resonance imaging structural magnetic resonance imaging data fusion canonical correlation analysis data

Abstract:: Abstract: In order to fully extract the magnetic resonance imaging (MRI) information of patients with major depressive disorder for improving the diagnostic accuracy of the disease, taking functional MRI data and structural MRI data as the research objects, a bimodal data fusion algorithm is proposed for the classification of major depressive disorder. After extracting the functional MRI data features by 4 kinds of functional brain networks with different scales, and obtaining the structural MRI data features by three-dimensional densely connected convolutional neural network processed by transfer learning, canonical correlation analysis method is used to fuse the two kinds of features, and finally support vector machine is utilized to classify the fusion features, thereby identifying the subjects as healthy or depressed. The experimental results show that the proposed method can obtain a classification accuracy of 89.56% and a recall rate of 95.48%. Compared with classification methods based on unimodal data, the classification algorithm based on bimodal data fusion has better classification performance. In addition, canonical correlation analysis is effective to fuse bimodal image features.