{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:49:49Z","timestamp":1787028589243,"version":"build-2736575974"},"reference-count":47,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:00:00Z","timestamp":1770336000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Future Artificial Intelligence Research","award":["FAIR CUP B53C22003630006"],"award-info":[{"award-number":["FAIR CUP B53C22003630006"]}]},{"DOI":"10.13039\/501100003407","name":"Italian Ministry for Universities and Research","doi-asserted-by":"crossref","award":["MUR: PE0000013"],"award-info":[{"award-number":["MUR: PE0000013"]}],"id":[{"id":"10.13039\/501100003407","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Computer-aided diagnosis (CAD) systems based on deep learning have shown significant potential for Alzheimer\u2019s disease (AD) stage classification from Magnetic Resonance Imaging (MRI). Nevertheless, challenges such as class imbalance, small sample sizes, and the presence of multiple slices per subject may lead to biased evaluation and statistically unreliable performance, particularly for minority classes. In this study, a Vision Transformer (ViT)-based framework is proposed for multi-class AD classification using a Kaggle dataset containing 6400 MRI slices across four cognitive stages. A subject-wise data-splitting strategy is employed to prevent information leakage between the training and testing sets, and the statistical unreliability of near-perfect scores in underrepresented classes is critically examined. An ablation study is conducted to assess the contribution of key architectural components, demonstrating the effectiveness of self-attention and patch embedding in capturing discriminative features. Furthermore, attention-based visualization maps are incorporated to highlight brain regions influencing the model\u2019s decisions and to illustrate subtle anatomical differences between MildDemented and VeryMildDemented cases. The proposed approach achieves a test accuracy of 97.98%, outperforming existing methods on the same dataset while providing improved interpretability. It supports early and accurate AD stage identification.<\/jats:p>","DOI":"10.3390\/info17020163","type":"journal-article","created":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T10:05:47Z","timestamp":1770372347000},"page":"163","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing Early Detection of Alzheimer\u2019s Disease via Vision Transformer Machine Learning Architecture Using MRI Images"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1299-1804","authenticated-orcid":false,"given":"Wided","family":"Hechkel","sequence":"first","affiliation":[{"name":"Laboratory of Micro-Optoelectronics and Nanostructures (LMON), University of Monastir, Avenue of the Environment, Monastir 5019, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5636-6130","authenticated-orcid":false,"given":"Marco","family":"Leo","sequence":"additional","affiliation":[{"name":"National Research Council of Italy, Institute of Applied Sciences and Intelligent Systems, 73100 Lecce, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3447-2922","authenticated-orcid":false,"given":"Pierluigi","family":"Carcagn\u00ec","sequence":"additional","affiliation":[{"name":"National Research Council of Italy, Institute of Applied Sciences and Intelligent Systems, 73100 Lecce, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0321-5798","authenticated-orcid":false,"given":"Marco","family":"Del-Coco","sequence":"additional","affiliation":[{"name":"National Research Council of Italy, Institute of Applied Sciences and Intelligent Systems, 73100 Lecce, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1623-6830","authenticated-orcid":false,"given":"Abdelhamid","family":"Helali","sequence":"additional","affiliation":[{"name":"Laboratory of Micro-Optoelectronics and Nanostructures (LMON), University of Monastir, Avenue of the Environment, Monastir 5019, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1186\/s13024-019-0333-5","article-title":"The neuropathological diagnosis of Alzheimer\u2019s disease","volume":"14","author":"Dickson","year":"2019","journal-title":"Mol. 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