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Because Alzheimer\u2019s disease (AD) has the characteristics of high incidence and high disability, it has attracted the attention of many scholars, and its diagnosis and treatment have gradually become a hot topic. In this paper, a multimodal diagnosis method for AD based on three\u2010dimensional shufflenet (3DShuffleNet) and principal component analysis network (PCANet) is proposed. First, the data on structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) are preprocessed to remove the influence resulting from the differences in image size and shape of different individuals, head movement, noise, and so on. Then, the original two\u2010dimensional (2D) ShuffleNet is developed three\u2010dimensional (3D), which is more suitable for 3D sMRI data to extract the features. In addition, the PCANet network is applied to the brain function connection analysis, and the features on fMRI data are obtained. Next, kernel canonical correlation analysis (KCCA) is used to fuse the features coming from sMRI and fMRI, respectively. Finally, a good classification effect is obtained through the support vector machines (SVM) method classifier, which proves the feasibility and effectiveness of the proposed method.<\/jats:p>","DOI":"10.1155\/2021\/6626728","type":"journal-article","created":{"date-parts":[[2021,4,28]],"date-time":"2021-04-28T18:42:01Z","timestamp":1619635321000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Assisted Diagnosis of Alzheimer\u2019s Disease Based on Deep Learning and Multimodal Feature Fusion"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2210-7461","authenticated-orcid":false,"given":"Yu","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chongchong","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,4,28]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00234-007-0269-2"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1212\/wnl.0b013e31823a0ef7"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/9151670"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/1645479"},{"key":"e_1_2_10_5_2","doi-asserted-by":"crossref","unstructured":"Hosseini-AslE. KeyntoR. andEl-BazA. Alzheimer\u2032s disease diagnostics by adaptation of 3D convolutional network Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP) September 2016 Phoenix Arizona USA.","DOI":"10.1109\/ICIP.2016.7532332"},{"key":"e_1_2_10_6_2","first-page":"355","article-title":"Predicting Alzheimer\u2032s disease: a neuroimaging study with 3D convolutional neural networks","volume":"2","author":"Payan A.","year":"2015","journal-title":"Computer Science"},{"key":"e_1_2_10_7_2","first-page":"1","article-title":"The diagnosis of Alzheimer\u2032s disease classification based on multi-scale residual neutral network","volume":"48","author":"Liu C. B.","year":"2018","journal-title":"Journal of Shandong University (Engineering Science)"},{"key":"e_1_2_10_8_2","doi-asserted-by":"crossref","unstructured":"BillonesC. D. DemetriaO. J. L. D. HostalleroD. E. D.et al. 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