{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,7]],"date-time":"2026-11-07T06:08:13Z","timestamp":1794031693522,"version":"4.8.0"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T00:00:00Z","timestamp":1750118400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Alzheimer\u2019s is a disease in the human brain characterized by gradual memory loss, confusion, and alterations in behavior. It is a complex and continuously degenerative disorder of the nervous system, which still has early detection and diagnosis as challenges to overcome. The disease causes significant damage in individuals suffering from the disorder as they progressively lose cognitive ability. Its diagnosis and management depend primarily on the ability to diagnose early to initiate proper intervention. Unfortunately, this remains a difficult feat, given the resemblance of early signs of the disease with symptoms associated with normal aging and other disorders involving cognition. While clinical tests have their limitations, brain imaging such as MRI can provide detailed insights into changes in the brain. Deep learning techniques, mainly when applied to MRI data, have proven helpful in the early detection of Alzheimer\u2019s Disease.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In the proposed study, a lightweight, self-attention-based vision transformer (ViT) is employed to predict Alzheimer\u2019s disease using MRI images from the OASIS-3 dataset. Data pre-processing and augmentation techniques have been added to strengthen the model\u2019s generalization ability and enhance model performance, which is visualized using Grad-Cam.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The proposed model achieves exceptional results with an accuracy of 98.57%, approximate precision of 98.7%, Recall of about 98.47%, and specificity of 98.67%. It also achieves a Kappa Score of 97.2% and an AUC ROC Score of 99%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This paper, along with comprehensive data pre-processing and augmentation, represents one of the major steps toward achieving more robust and clinically applicable models for Alzheimer\u2019s disease prediction. The proposed study indicates that deep learning models have the potential to enhance the diagnosis of Alzheimer\u2019s disease. By integrating Deep learning techniques with careful data processing, more reliable early detection models can be developed, which in turn leads to better treatment outcomes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2025.1568312","type":"journal-article","created":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T06:33:22Z","timestamp":1750142002000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["AlzheimerViT: harnessing lightweight vision transformer architecture for proactive Alzheimer\u2019s screening"],"prefix":"10.3389","volume":"12","author":[{"given":"C.","family":"Kishor Kumar Reddy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hafsa Ihteshamuddin","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Mohzary","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"T.","family":"Monika Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed","family":"Shuaib","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shadab","family":"Alam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hani Mohammed","family":"Alnami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,6,17]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1080\/00207454.2021.1901696","article-title":"Efficient optimization based thresholding technique for analysis of Alzheimer MRIs","volume":"133","author":"Prabha","year":"2023","journal-title":"Int J Neurosci"},{"key":"ref2","year":""},{"key":"ref3","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/J.CLINBIOCHEM.2019.04.015","article-title":"Clinical aspects of Alzheimer\u2019s disease","volume":"72","author":"Zv\u011b\u0159ov\u00e1","year":"2019","journal-title":"Clin Biochem"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"199","DOI":"10.2147\/JIR.S86958","article-title":"Targeting Neuroinflammation in Alzheimer\u2019s disease","volume":"9","author":"Bronzuoli","year":"2016","journal-title":"J Inflamm Res"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1177\/1715163517690745","article-title":"Dementia: what pharmacists need to know","volume":"150","author":"Duong","year":"2017","journal-title":"Can Pharm J"},{"key":"ref6","year":""},{"key":"ref7","doi-asserted-by":"publisher","first-page":"2237","DOI":"10.3390\/diagnostics14192237","article-title":"Improving Alzheimer\u2019s disease prediction with different machine learning approaches and feature selection techniques","volume":"14","author":"Alshamlan","year":"2024","journal-title":"Diagnostics"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"15","DOI":"10.21786\/bbrc\/13.11\/4","article-title":"Alzheimer disease forecasting using machine learning algorithm","volume":"13","author":"Malavika","year":"2020","journal-title":"Biosci Biotechnol Res Commun"},{"key":"ref9","doi-asserted-by":"publisher","first-page":"8625","DOI":"10.1002\/ALZ.14305","article-title":"Individualized, cross-validated prediction of future dementia using cognitive assessments in people with mild cognitive symptoms","volume":"20","author":"Borland","year":"2024","journal-title":"Alzheimers Dement"},{"key":"ref10","doi-asserted-by":"crossref","DOI":"10.1109\/AIKIIE60097.2023.10389821","article-title":"Analysis of dementia prognostication using computational models","author":"Patil","year":"2023"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"4623","DOI":"10.11591\/ijece.v13i4.pp4623-4632","article-title":"Prediction of dementia using machine learning model and performance improvement with cuckoo algorithm","volume":"13","author":"Rajayyan","year":"2023","journal-title":"Int J Electr Comput Eng"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"651","DOI":"10.3390\/APP7070651","article-title":"Early diagnosis of dementia from clinical data by machine learning techniques","volume":"7","author":"So","year":"2017","journal-title":"Appl Sci"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"1464","DOI":"10.3390\/APP13031464","article-title":"Dementia classification using deep reinforcement learning for early diagnosis","volume":"13","author":"Hashmi","year":"2023","journal-title":"Appl Sci"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"21451","DOI":"10.1007\/S00521-024-10317-9","article-title":"Dementia diagnosis in young adults: a machine learning and optimization approach","volume":"36","author":"Talaat","year":"2024","journal-title":"Neural Comput Applic"},{"key":"ref15","doi-asserted-by":"crossref","DOI":"10.1109\/ICCCI56745.2023.10128411","article-title":"The early detection of dementia disease using machine learning approach","author":"Kabir","year":"2023"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"853294","DOI":"10.3389\/fpubh.2022.853294","article-title":"Early-stage Alzheimer\u2019s disease prediction using machine learning models","volume":"10","author":"Kavitha","year":"2022","journal-title":"Front Public Health"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"371","DOI":"10.14283\/JPAD.2021.23","article-title":"Diagnosis of early Alzheimer\u2019s disease: clinical practice in 2021","volume":"8","author":"Porsteinsson","year":"2021","journal-title":"J Prev Alzheimers Dis"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-74399-w","article-title":"Multimodal deep learning models for early detection of Alzheimer\u2019s disease stage","volume":"11","author":"Venugopalan","year":"2021","journal-title":"Sci. Rep."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/9917919","article-title":"A comparative analysis of machine learning algorithms to predict Alzheimer\u2019s disease","volume":"2021","author":"Bari Antor","year":"2021","journal-title":"J Healthc Eng"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"42449","DOI":"10.1109\/ACCESS.2021.3066213","article-title":"Computational modeling of dementia prediction using deep neural network: analysis on OASIS dataset","volume":"9","author":"Basheer","year":"2021","journal-title":"IEEE Access"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"e0230409","DOI":"10.1371\/JOURNAL.PONE.0230409","article-title":"Alzheimer\u2019s disease diagnosis from diffusion tensor images using convolutional neural networks","volume":"15","author":"Marzban","year":"2020","journal-title":"PLoS One"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"22252","DOI":"10.1038\/S41598-020-79243-9","article-title":"Identification of Alzheimer\u2019s disease using a convolutional neural network model based on T1-weighted magnetic resonance imaging","volume":"10","author":"Bin","year":"2020","journal-title":"Sci Rep"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"67","DOI":"10.2174\/1567205014666170922101135","article-title":"Neuroimaging initiative, for the a.D. discriminative sparse features for Alzheimer\u2019s disease diagnosis using multimodal image data","volume":"15","author":"Ortiz","year":"2018","journal-title":"Curr Alzheimer Res"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2110.02178","article-title":"MobileViT: Lightweight, Generalpurpose, and Mobile-friendly vision transformer","author":"Mehta","year":"2021","journal-title":"Arxiv"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2010.11929","article-title":"An image is worth 16x16 words: transformers for image recognition at scale","author":"Dosovitskiy","year":"2020","journal-title":"ArXiv"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"4017","DOI":"10.1109\/TNSRE.2023.3323432","article-title":"Brain structure-function fusing representation learning using adversarial decomposed-VAE for analyzing MCI","volume":"31","author":"Zuo","year":"2023","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"ref27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TCE.2025.3528438","article-title":"Generative AI enables EEG super-resolution via Spatio-temporal adaptive diffusion learning","volume":"99","author":"Wang","year":"2025","journal-title":"IEEE Trans Consum Electron"},{"key":"ref28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/5629090","article-title":"Machine learning for the preliminary diagnosis of dementia","volume":"2020","author":"Zhu","year":"2020","journal-title":"Sci Program"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3439950","article-title":"Van Den deep learning for anomaly detection: a review","volume":"54","author":"Pang","year":"2021","journal-title":"ACM Comput Surv"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"5998","DOI":"10.17762\/TURCOMAT.V12I11.6894","article-title":"Alzheimer disease detection and classification on magnetic resonance imaging (Mri) brain images using improved expectation maximization (Iem) and convolutional neural network (Cnn). Turkish J. Comput","volume":"12","author":"Ramana","year":"2021","journal-title":"Math Educ"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1007\/s42979-022-01371-y","article-title":"Machine learning workflow to explain black-box models for early Alzheimer\u2019s disease classification evaluated for multiple datasets","volume":"3","author":"Bloch","year":"2022","journal-title":"SN Comput. Sci."},{"key":"ref32","doi-asserted-by":"publisher","first-page":"212","DOI":"10.3390\/brainsci9090212","article-title":"Classification of Alzheimer\u2019s disease with and without imagery using gradient boosted machines and ResNet-50","volume":"9","author":"Fulton","year":"2019","journal-title":"Brain Sci"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"5896","DOI":"10.15419\/bmrat.v10i9.832","article-title":"Early Alzheimer?S disease diagnosis using an XG-boost model applied to MRI images","volume":"10","author":"Nguyen","year":"2023","journal-title":"Biomed Res Ther"},{"key":"ref34","doi-asserted-by":"publisher","first-page":"307","DOI":"10.3389\/fnins.2015.00307","article-title":"Alzheimer's disease neuroimaging initiative. Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer\u2019s disease: a machine learning approach","volume":"9","author":"Salvatore","year":"2015","journal-title":"Front Neurosci"},{"key":"ref35","doi-asserted-by":"publisher","first-page":"4902","DOI":"10.17051\/ilkonline.2021.03.288","article-title":"A hybrid machine learning framework for biomarkers based ADNI disease prediction","volume":"20","author":"Krishna","year":"2021","journal-title":"Ilkogr Online"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"e0277322","DOI":"10.1371\/JOURNAL.PONE.0277322","article-title":"Machine learning based multi-modal prediction of future decline toward Alzheimer\u2019s disease: an empirical study","volume":"17","author":"Karaman","year":"2022","journal-title":"PLoS One"}],"container-title":["Frontiers in Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2025.1568312\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T06:33:23Z","timestamp":1750142003000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2025.1568312\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,17]]},"references-count":36,"alternative-id":["10.3389\/fmed.2025.1568312"],"URL":"https:\/\/doi.org\/10.3389\/fmed.2025.1568312","relation":{},"ISSN":["2296-858X"],"issn-type":[{"value":"2296-858X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,17]]},"article-number":"1568312"}}