Alzheimer's disease classification method based on multi-modal medical images

被引：0

作者：

Han K. ^{[1
]}

Pan H. ^{[1
]}

Zhang W. ^{[2
]}

Bian X. ^{[1
]}

Chen C. ^{[1
]}

He S. ^{[1
]}

机构：

[1] College of Computer Science and Technology, Harbin Engineering University, Harbin

[2] Modern Education Technology Center, Heilongjiang University, Harbin

来源：

Qinghua Daxue Xuebao/Journal of Tsinghua University | 2020年 / 60卷 / 08期

关键词：

Alzheimer's disease (AD); Convolutional neural networks; Deep learning; Image classification; Magnetic resonance imaging (MRI); Positron emission computed tomography (PET);

D O I：

10.16511/j.cnki.qhdxxb.2020.25.003

中图分类号：

学科分类号：

摘要：

Multi-modal medical image information has been widely used in computer-aided detection and computer-aided diagnosis in the medical community. Feature information from multi-modal medical images can be used to accurately classify and diagnosis Alzheimer's disease (AD) characteristics. This paper presents a convolutional neural network model for 3D convolution operations on magnetic resonance imaging (MRI) and positron emission computed tomography (PET) images of Alzheimer's subjects to extract the feature information for the various modalities. Then, models are used to fuse these modal information sets into a rich multi-modal feature information dataset. Finally, this dataset is classified and predicted using a fully connected neural network. Tests on the public data set of the AD neuroimaging initiative show that this model more accurately evaluates accuracy (ACC) and area under the curve (AUC) conditions. © 2020, Tsinghua University Press. All right reserved.

引用

页码：664 / 671and682

共 20 条

[1]

ZHAN L, ZHOU J Y, WANG Y L, Et al., Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer's disease, Frontiers in Aging Neuroscience, 7, (2015)

[2]

CHRISTINA P., World Alzheimer report 2018, (2018)

[3]

PETERSEN R C., Mild cognitive impairment, The Lancet, 367, 9527, (2006)

[4]

LITJENS G, SANCHEZ C I, TIMOFEEVA N, Et al., Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis, Nature Scientific Reports, 6, (2016)

[5]

GAO L L, PAN H W, LI Q, Et al., Brain medical image diagnosis based on corners with importance-values, BMC Bioinformatics, 18, (2017)

[6]

GAO L L, PAN H W, XIE X Q, Et al., Graph modeling and mining methods for brain images, Multimedia Tools and Applications, 75, 15, pp. 9333-9369, (2016)

[7]

ZHANG D, SHEN D., Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease, NeuroImage, 59, 2, pp. 895-907, (2012)

[8]

KOROLEV S, SAFIULLIN A, BELYAEV M, Et al., Residual and plain convolutional neural networks for 3D brain MRI classification, 2017 IEEE 14th International Symposium on Biomedical Imaging, 4, pp. 18-21, (2017)

[9]

BRON E E, SMITS M, VAN DER FLIER W M, Et al., Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CAD dementia challenge, NeuroImage, 111, pp. 562-579, (2015)

[10]

GRAY K R, WOLZ R, HECKEMANN R A, Et al., Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer's disease, NeuroImage, 60, 1, pp. 221-229, (2012)

← 1 2 →