{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:25:07Z","timestamp":1775067907660,"version":"3.50.1"},"reference-count":278,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T00:00:00Z","timestamp":1708473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Alzheimer\u2019s disease (AD) is a pressing global issue, demanding effective diagnostic approaches. This systematic review surveys the recent literature (2018 onwards) to illuminate the current landscape of AD detection via deep learning. Focusing on neuroimaging, this study explores single- and multi-modality investigations, delving into biomarkers, features, and preprocessing techniques. Various deep models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative models, are evaluated for their AD detection performance. Challenges such as limited datasets and training procedures persist. Emphasis is placed on the need to differentiate AD from similar brain patterns, necessitating discriminative feature representations. This review highlights deep learning\u2019s potential and limitations in AD detection, underscoring dataset importance. Future directions involve benchmark platform development for streamlined comparisons. In conclusion, while deep learning holds promise for accurate AD detection, refining models and methods is crucial to tackle challenges and enhance diagnostic precision.<\/jats:p>","DOI":"10.3390\/make6010024","type":"journal-article","created":{"date-parts":[[2024,2,21]],"date-time":"2024-02-21T11:35:04Z","timestamp":1708515304000},"page":"464-505","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":98,"title":["Alzheimer\u2019s Disease Detection Using Deep Learning on Neuroimaging: A Systematic Review"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0880-5466","authenticated-orcid":false,"given":"Mohammed G.","family":"Alsubaie","sequence":"first","affiliation":[{"name":"School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia"},{"name":"Department of Computer Science, College of Khurma University College, Taif University, Taif 21944, Saudi Arabia"}]},{"given":"Suhuai","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia"}]},{"given":"Kamran","family":"Shaukat","sequence":"additional","affiliation":[{"name":"School of Information and Physical Sciences, The University of Newcastle, Newcastle, NSW 2308, Australia"},{"name":"Centre for Artificial Intelligence Research and Optimisation, Design and Creative Technology Vertical, Torrens University, Ultimo, Sydney, NSW 2007, Australia"},{"name":"Department of Data Science, University of the Punjab, Lahore 54890, Pakistan"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1038\/nature08984","article-title":"Biodemography of human ageing","volume":"464","author":"Vaupel","year":"2010","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.gloenvcha.2014.06.004","article-title":"The human core of the shared socioeconomic pathways: Population scenarios by age, sex and level of education for all countries to 2100","volume":"42","author":"Samir","year":"2017","journal-title":"Glob. 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