{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T23:11:00Z","timestamp":1790464260000,"version":"4.1.0"},"reference-count":48,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T00:00:00Z","timestamp":1739491200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82001160"],"award-info":[{"award-number":["82001160"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017544","name":"Health and Family Planning Commission of Lianyungang City","doi-asserted-by":"publisher","award":["2017-QD-009"],"award-info":[{"award-number":["2017-QD-009"]}],"id":[{"id":"10.13039\/501100017544","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100016305","name":"First Affiliated Hospital of Zhengzhou University","doi-asserted-by":"publisher","award":["XJ1811"],"award-info":[{"award-number":["XJ1811"]}],"id":[{"id":"10.13039\/501100016305","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hong Kong Polytechnic University Start-up Fund","award":["P0053210"],"award-info":[{"award-number":["P0053210"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Alzheimer\u2019s disease (AD) is a leading cause of disability worldwide. Early detection is critical for preventing progression and formulating effective treatment plans. This study aims to develop a novel deep learning (DL) model, Hybrid-RViT, to enhance the detection of AD. The proposed Hybrid-RViT model integrates the pre-trained convolutional neural network (ResNet-50) with the Vision Transformer (ViT) to classify brain MRI images across different stages of AD. The ResNet-50 adopted for transfer learning, facilitates inductive bias and feature extraction. Concurrently, ViT processes sequences of image patches to capture long-distance relationships via a self-attention mechanism, thereby functioning as a joint local-global feature extractor. The Hybrid-RViT model achieved a training accuracy of 97% and a testing accuracy of 95%, outperforming previous models. This demonstrates its potential efficacy in accurately identifying and classifying AD stages from brain MRI data. The Hybrid-RViT model, combining ResNet-50 and ViT, shows superior performance in AD detection, highlighting its potential as a valuable tool for medical professionals in interpreting and analyzing brain MRI images. This model could significantly improve early diagnosis and intervention strategies for AD.<\/jats:p>","DOI":"10.1371\/journal.pone.0318998","type":"journal-article","created":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T13:26:24Z","timestamp":1739539584000},"page":"e0318998","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":47,"title":["Hybrid-RViT: Hybridizing ResNet-50 and Vision Transformer for Enhanced Alzheimer\u2019s disease detection"],"prefix":"10.1371","volume":"20","author":[{"given":"Hongjie","family":"Yan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vivens","family":"Mubonanyikuzo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4161-0158","authenticated-orcid":true,"given":"Temitope Emmanuel","family":"Komolafe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9701-2918","authenticated-orcid":true,"given":"Nizhuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,2,14]]},"reference":[{"issue":"2010","key":"pone.0318998.ref001","first-page":"484","article-title":"Alzheimer disease","volume":"9","author":"Rudy J. 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