{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T15:37:01Z","timestamp":1784907421538,"version":"3.55.0"},"reference-count":26,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Alzheimer's Disease Reports"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Significant advancements in neuroimaging have emerged over the past decade, notably through positron emission tomography (PET) and magnetic resonance imaging (MRI) for diagnosing Alzheimer's disease (AD) and its precursor, mild cognitive impairment (MCI). Combining imaging modalities with machine learning (ML) techniques enhances diagnostic accuracy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop predictive models using pre-treatment brain imaging data to distinguish between normal controls (NC), MCI, and AD stages, improving diagnostic precision.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We utilized the Alzheimer's Disease Neuroimaging Initiative database, processing 3D MRI, PET Florbetaben, and PET Flortaucipir images. Techniques included convolutional neural networks (CNN), fuzzy logic, and multi-layer perceptron (MLP). Feature extraction involved amyloid-\u03b2 volume, tau protein levels, and empty space volumes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The fuzzy logic approach achieved a classification accuracy of 99.1%, outperforming CNN (90.67%) and MLP (94%). Integration of multimodal data significantly enhanced performance compared to single-modality approaches.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our study demonstrates that integrating advanced ML techniques with multimodal neuroimaging can effectively classify AD stages. These findings address critical gaps in early detection and provide a foundation for future clinical applications.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/25424823241311838","type":"journal-article","created":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T04:22:19Z","timestamp":1736742139000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Alzheimer's disease classification by supervised and intelligent techniques"],"prefix":"10.1177","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8605-0129","authenticated-orcid":false,"given":"Jabli","family":"Mohamed Amine","sequence":"first","affiliation":[{"name":"NOCCS Laboratory, l\u2019Ecole National d\u2019Ing\u00e9nieurs de Sousse ENISO, Universit\u00e9 de Sousse, P\u00f4le technologique de Sousse, Sousse, Tunisie"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moussa","family":"Mourad","sequence":"additional","affiliation":[{"name":"NOCCS Laboratory, l\u2019Ecole National d\u2019Ing\u00e9nieurs de Sousse ENISO, Universit\u00e9 de Sousse, P\u00f4le technologique de Sousse, Sousse, Tunisie"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"for the Alzheimer's Disease Neuroimaging Initiative (ADNI)","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,1,13]]},"reference":[{"key":"bibr1-25424823241311838","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(07)70178-3"},{"key":"bibr2-25424823241311838","doi-asserted-by":"publisher","DOI":"10.1016\/j.neurobiolaging.2010.05.023"},{"key":"bibr3-25424823241311838","doi-asserted-by":"crossref","unstructured":"Cai W, Liu S, Song Y, et al. A 3D difference of Gaussian-based lesion detector for brain PET. In: 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014, Beijing, China, 29 April 2014-2 May 2014, pp.677\u2013680, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE).","DOI":"10.1109\/ISBI.2014.6867961"},{"key":"bibr4-25424823241311838","doi-asserted-by":"publisher","DOI":"10.1212\/01.WNL.0000055847.17752.E6"},{"key":"bibr5-25424823241311838","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2014.05.003"},{"key":"bibr6-25424823241311838","doi-asserted-by":"publisher","DOI":"10.1016\/j.neurobiolaging.2006.05.006"},{"key":"bibr7-25424823241311838","doi-asserted-by":"publisher","DOI":"10.2174\/156720509788929273"},{"key":"bibr8-25424823241311838","doi-asserted-by":"crossref","unstructured":"Cai W, Liu S, Wen L, et al. 3D Neurological image retrieval with localized pathology-centric CMRGlc patterns. 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Alzheimer's disease diagnostics by adaptation of 3D convolutional network. In: Proceedings of the Medical Image Computing and Computer-Assisted Intervention, Munich, Germany, 5\u20139 October 2016, pp.238\u2013247, Cham, Switzerland: Springer.","DOI":"10.1109\/ICIP.2016.7532332"},{"key":"bibr23-25424823241311838","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2018.09.009"},{"key":"bibr24-25424823241311838","doi-asserted-by":"publisher","DOI":"10.1016\/S0019-9958(65)90241-X"},{"key":"bibr25-25424823241311838","doi-asserted-by":"publisher","DOI":"10.1016\/S0019-9958(68)90211-8"},{"key":"bibr26-25424823241311838","doi-asserted-by":"crossref","unstructured":"Krashenyi I, Popov A, Ramirez J, et al. Fuzzy classification of Alzheimer's disease using statistical moments. 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