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

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. Howe...

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Autores principales:	Zijian Wang, Yaqin Zhu, Haibo Shi, Yanting Zhang, Cairong Yan
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	convolutional neural netwoks attention mri deep learning adhd schizophrenia Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/c4271e5bb06e44cdb35d5545d2bd5412
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id	oai:doaj.org-article:c4271e5bb06e44cdb35d5545d2bd5412
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spelling	oai:doaj.org-article:c4271e5bb06e44cdb35d5545d2bd54122021-11-23T01:33:54ZA 3D multiscale view convolutional neural network with attention for mental disease diagnosis on MRI images10.3934/mbe.20213471551-0018https://doaj.org/article/c4271e5bb06e44cdb35d5545d2bd54122021-08-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021347?viewType=HTMLhttps://doaj.org/toc/1551-0018Computer 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.Zijian WangYaqin ZhuHaibo ShiYanting Zhang Cairong Yan AIMS Pressarticleconvolutional neural netwoksattentionmrideep learningadhdschizophreniaBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 5, Pp 6978-6994 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	convolutional neural netwoks attention mri deep learning adhd schizophrenia Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	convolutional neural netwoks attention mri deep learning adhd schizophrenia Biotechnology TP248.13-248.65 Mathematics QA1-939 Zijian Wang Yaqin Zhu Haibo Shi Yanting Zhang Cairong Yan A 3D multiscale view convolutional neural network with attention for mental disease diagnosis on MRI images
description	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.
format	article
author	Zijian Wang Yaqin Zhu Haibo Shi Yanting Zhang Cairong Yan
author_facet	Zijian Wang Yaqin Zhu Haibo Shi Yanting Zhang Cairong Yan
author_sort	Zijian Wang
title	A 3D multiscale view convolutional neural network with attention for mental disease diagnosis on MRI images
title_short	A 3D multiscale view convolutional neural network with attention for mental disease diagnosis on MRI images
title_full	A 3D multiscale view convolutional neural network with attention for mental disease diagnosis on MRI images
title_fullStr	A 3D multiscale view convolutional neural network with attention for mental disease diagnosis on MRI images
title_full_unstemmed	A 3D multiscale view convolutional neural network with attention for mental disease diagnosis on MRI images
title_sort	3d multiscale view convolutional neural network with attention for mental disease diagnosis on mri images
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/c4271e5bb06e44cdb35d5545d2bd5412
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A 3D multiscale view convolutional neural network with attention for mental disease diagnosis on MRI images

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