{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:42:34Z","timestamp":1783438954513,"version":"3.54.6"},"reference-count":30,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T00:00:00Z","timestamp":1628899200000},"content-version":"vor","delay-in-days":225,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Alzheimer\u2019s disease (AD) is an irreversible neurodegenerative disease, and, at present, once it has been diagnosed, there is no effective curative treatment. Accurate and early diagnosis of Alzheimer\u2019s disease is crucial for improving the condition of patients since effective preventive measures can be taken in advance to delay the onset time of the disease. <jats:sup>18<\/jats:sup>F\u2010Fluorodeoxyglucose positron emission tomography (<jats:sup>18<\/jats:sup>F\u2010FDG PET\u2009:\u2009PET) is an effective biomarker of the symptom of AD and has been used as medical imaging data for diagnosing AD. Mild cognitive impairment (MCI) is regarded as an early symptom of AD, and it has been shown that MCI also has a certain biomedical correlation with PET. In this paper, we explore how to use 3D PET images to realize the effective recognition of MCI and thus achieve the early prediction of AD. This problem is then taken as the classification of three categories of PET images, including MCI, AD, and NC (normal controls). In order to get better classification performance, a novel network model is proposed in the paper based on 3D convolution neural networks (CNN) and support vector machines (SVM) by utilizing both the excellent abilities of CNN in feature extraction and SVM in classification. In order to make full use of the optimal property of SVM in solving binary classification problems, the three\u2010category classification problem is divided into three binary classifications, and each binary classification is being realized with a CNN\u2009+\u2009SVM network. Then, the outputs of the three CNN\u2009+\u2009SVM networks are fused into a final three\u2010category classification result. An end\u2010to\u2010end learning algorithm is developed to train the CNN\u2009+\u2009SVM networks, and a decision fusion algorithm is exploited to realize the fusion of the outputs of three CNN\u2009+\u2009SVM networks. Experimental results obtained in the work with comparative analyses confirm the effectiveness of the proposed method.<\/jats:p>","DOI":"10.1155\/2021\/9121770","type":"journal-article","created":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T23:05:42Z","timestamp":1628982342000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Automatic Diagnosis of Alzheimer\u2019s Disease and Mild Cognitive Impairment Based on CNN\u2009+\u2009SVM Networks with End\u2010to\u2010End Training"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3794-9749","authenticated-orcid":false,"given":"Zhe","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7125-4866","authenticated-orcid":false,"given":"Minglang","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3567-1416","authenticated-orcid":false,"given":"Chengan","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,8,14]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2019.01.010"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24888-2_31"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1002\/ana.20799"},{"key":"e_1_2_8_4_2","doi-asserted-by":"publisher","DOI":"10.3233\/jad-2010-091504"},{"key":"e_1_2_8_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/s1474-4422(09)70299-6"},{"key":"e_1_2_8_6_2","doi-asserted-by":"crossref","unstructured":"GrayK. R. WolzR. KeihaninejadS.et al. Regional analysis of FDG-PET for use in the classification of Alzheimer\u2019s disease Proceedings of the 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro March 2011 Chicago IL USA 1082\u20131085 https:\/\/doi.org\/10.1109\/ISBI.2011.5872589 2-s2.0-80055054309.","DOI":"10.1109\/ISBI.2011.5872589"},{"key":"e_1_2_8_7_2","doi-asserted-by":"crossref","unstructured":"GaraliI. AdelM. BourennaneS. andGuedjE. Region-based brain selection and classification on PET images for Alzheimer\u2019s disease computer aided diagnosis Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP) September 2015 Quebec City Canada 1473\u20131477 https:\/\/doi.org\/10.1109\/icip.2015.7351045 2-s2.0-84956707184.","DOI":"10.1109\/ICIP.2015.7351045"},{"key":"e_1_2_8_8_2","doi-asserted-by":"crossref","unstructured":"SilveiraM.andMarquesJ. S. Boosting Alzheimer\u2019s disease diagnosis using PET images Proceedings of the 2010 20th International Conference on Pattern Recognition August 2010 Istanbul Turkey 23\u201326 https:\/\/doi.org\/10.1109\/ICPR.2010.626 2-s2.0-78149472829.","DOI":"10.1109\/ICPR.2010.626"},{"key":"e_1_2_8_9_2","doi-asserted-by":"crossref","unstructured":"CabralC.andSilveiraM. Classification of Alzheimer\u2019s disease from FDG-PET images using favorite class ensembles Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society July 2013 Osaka Japan 2477\u20132480 https:\/\/doi.org\/10.1109\/EMBC.2013.6610042 2-s2.0-84886573900.","DOI":"10.1109\/EMBC.2013.6610042"},{"key":"e_1_2_8_10_2","doi-asserted-by":"crossref","unstructured":"LuS. XiaY. CaiT. W.et al. Semi-supervised manifold learning with affinity regularization for Alzheimer\u2032s disease identification using positron emission tomography imaging Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) August 2015 Milan Italy 2251\u20132254 https:\/\/doi.org\/10.1109\/EMBC.2015.7318840 2-s2.0-84953285190.","DOI":"10.1109\/EMBC.2015.7318840"},{"key":"e_1_2_8_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-018-0932-7"},{"key":"e_1_2_8_12_2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2018180958"},{"key":"e_1_2_8_13_2","doi-asserted-by":"crossref","unstructured":"SzegedyC. VanhouckeV. IoffeS.et al. Rethinking the inception architecture for computer vision Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) June 2016 Seattle WA USA 2818\u20132826 https:\/\/doi.org\/10.1109\/cvpr.2016.308 2-s2.0-84986296808.","DOI":"10.1109\/CVPR.2016.308"},{"key":"e_1_2_8_14_2","doi-asserted-by":"publisher","DOI":"10.3389\/fninf.2018.00035"},{"key":"e_1_2_8_15_2","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2019.00509"},{"key":"e_1_2_8_16_2","unstructured":"LiuS. YadavC. Fernandez-GrandaC.et al. On the design of convolutional neural networks for automatic detection of Alzheimer\u2019s disease 2020 https:\/\/arxiv.org\/abs\/1911.03740v3."},{"key":"e_1_2_8_17_2","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2020.621204"},{"key":"e_1_2_8_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2014.2372011"},{"key":"e_1_2_8_19_2","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.24783"},{"key":"e_1_2_8_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2020.3022591"},{"key":"e_1_2_8_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/bf00994018"},{"key":"e_1_2_8_22_2","unstructured":"UlyanovD. VedaldiA. andLempitskyV. Instance normalization: The missing ingredient for fast stylization 2016 https:\/\/arxiv.org\/abs\/1607.08022."},{"key":"e_1_2_8_23_2","doi-asserted-by":"crossref","unstructured":"WooS. ParkJ. LeeJ.-Y. andKweonI. S. CBAM: convolutional block attention module Proceedings of the European Conference on Computer Vision (ECCV) August 2018 Glasgow UK 3\u201319 https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1 2-s2.0-85055111544.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"e_1_2_8_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_2_8_25_2","unstructured":"HuangG. LiuZ. LaurensV. D. M.et al. Densely connected convolutional networks Proceedings the IEEE Conference on Computer Vision and Pattern Recognition January 2016 4700\u20134708."},{"key":"e_1_2_8_26_2","doi-asserted-by":"crossref","unstructured":"HeK. ZhangX. RenS. andSunJ. Deep residual learning for image recognition Proceedings the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) June 2016 Las Vegas NV USA 770\u2013778 https:\/\/doi.org\/10.1109\/cvpr.2016.90 2-s2.0-84986274465.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_8_27_2","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfit- ting","volume":"15","author":"Srivastava N.","year":"2014","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_8_28_2","unstructured":"MulerR. KornblithS. andHintonG. When does label smoothing help? 2019 https:\/\/arxiv.org\/abs\/1906.02629."},{"key":"e_1_2_8_29_2","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/1130.003.0016"},{"key":"e_1_2_8_30_2","volume-title":"ADNI Database","year":"2020"}],"container-title":["Computational Intelligence and Neuroscience"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/9121770.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cin\/2021\/9121770.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/9121770","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T11:56:51Z","timestamp":1722945411000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/9121770"}},"subtitle":[],"editor":[{"given":"Nian","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":30,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/9121770"],"URL":"https:\/\/doi.org\/10.1155\/2021\/9121770","archive":["Portico"],"relation":{},"ISSN":["1687-5265","1687-5273"],"issn-type":[{"value":"1687-5265","type":"print"},{"value":"1687-5273","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2021-05-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-08-06","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-08-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"9121770"}}