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A 3D multiscale view convolutional neural network with attention for mental disease diagnosis on MRI images.

Authors
  • Wang, Zijian1
  • Zhu, Yaqin1
  • Shi, Haibo2
  • Zhang, Yanting1
  • Yan, Cairong1
  • 1 School of Computer Science and Technology, Donghua University, Shanghai 200000, China. , (China)
  • 2 School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200000, China. , (China)
Type
Published Article
Journal
Mathematical biosciences and engineering : MBE
Publication Date
Aug 23, 2021
Volume
18
Issue
5
Pages
6978–6994
Identifiers
DOI: 10.3934/mbe.2021347
PMID: 34517567
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Computer Assisted Diagnosis (CAD) based on brain Magnetic Resonance Imaging (MRI) is a popular research field for the computer science and medical engineering. Traditional machine learning and deep learning methods were employed in the classification of brain MRI images in the previous studies. However, the current algorithms rarely take into consideration the influence of multi-scale brain connectivity disorders on some mental diseases. To improve this defect, a deep learning structure was proposed based on MRI images, which was designed to consider the brain's connections at different sizes and the attention of connections. In this work, a Multiscale View (MV) module was proposed, which was designed to detect multi-scale brain network disorders. On the basis of the MV module, the path attention module was also proposed to simulate the attention selection of the parallel paths in the MV module. Based on the two modules, we proposed a 3D Multiscale View Convolutional Neural Network with Attention (3D MVA-CNN) for classification of MRI images for mental disease. The proposed method outperformed the previous 3D CNN structures in the structural MRI data of ADHD-200 and the functional MRI data of schizophrenia. Finally, we also proposed a preliminary framework for clinical application using 3D CNN, and discussed its limitations on data accessing and reliability. This work promoted the assisted diagnosis of mental diseases based on deep learning and provided a novel 3D CNN method based on MRI data.

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