{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T07:15:27Z","timestamp":1788419727401,"version":"build-2803163510"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"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. Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Alzheimer\u2019s disease (AD) is a progressive neurodegenerative disorder that leads to a gradual decline in cognitive and memory function. Multimodal neuroimaging, particularly magnetic resonance imaging (MRI), has become a central diagnostic tool for tracking disease progression, supporting diagnosis, treatment planning, and follow-up monitoring. Manual delineation of brain structures by clinicians remains time-consuming and labor-intensive, and computer-assisted segmentation is complicated by spatial and structural variability as well as intensity inhomogeneity across images. This study proposes an integrated framework for automated brain image segmentation and AD classification to address these challenges.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The framework combines a modified U-Net architecture, termed FFA U-Net, with a fine-tuned VGG16 classifier. FFA U-Net incorporates a residual inception module and a feature fusion attention mechanism into the standard U-Net backbone to perform end-to-end brain tissue segmentation. Segmented outputs are subsequently passed to the fine-tuned VGG16 model for classification of AD status. The framework was evaluated on two datasets, ADNI (843 images) and OASIS (416 images), using subject-wise, non-overlapping training, validation, and test splits to prevent data leakage.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The proposed framework achieved a segmentation Dice Similarity Coefficient (DSC) of 0.929 and a classification accuracy of 98.90%, based on a single data split. These results were consistent across both the ADNI and OASIS datasets, indicating competitive segmentation and classification performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The findings suggest that combining attention-augmented segmentation with transfer learning-based classification can effectively support automated AD detection from structural MRI, potentially reducing the manual burden on clinicians. As results are based on a single split, further validation using cross-validation or independent cohorts is warranted to confirm robustness and generalizability. The framework should currently be regarded as an experimental research tool that requires additional clinical validation before deployment in practice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnins.2026.1871982","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T06:41:04Z","timestamp":1788417664000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Alzheimer\u2019s disease detection from structural brain MRI using FFA U-Net segmentation and transfer learning"],"prefix":"10.3389","volume":"20","author":[{"given":"Nirupama","family":"Panabakam","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, VEMU Institute of Technology","place":["Chitoor, Andhra Pradesh, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karthik","family":"Elangovan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi","place":["Chennai, Tamil Nadu, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Koteeswaran","family":"Seerangan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, R. M. K. Engineering College (Autonomous)","place":["Kavaraipettai, Tamil Nadu, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nalini","family":"Manogaran","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering (Cyber Security), S.A. Engineering College (Autonomous)","place":["Chennai, Tamil Nadu, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karthiga","family":"Malleeswaran","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bannari Amman Institute of Technology","place":["Sathyamangalam, Tamil Nadu, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sumendra","family":"Yogarayan","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology (FIST), Multimedia University (MMU)","place":["Melaka, Malaysia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,3]]},"reference":[{"key":"ref9001","doi-asserted-by":"publisher","first-page":"7259","DOI":"10.3390\/s21217259","article-title":"Transfer learning for Alzheimer\u2019s disease through neuroimaging biomarkers: a systematic review","volume":"21","author":"Agarwal","year":"2021","journal-title":"Sensors"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"100771","DOI":"10.1016\/j.simpa.2025.100771","article-title":"MRI-based Alzheimer\u2019s disease classification using vision transformer and time-series transformer: a step-by-step guide","volume":"25","author":"Alp","year":"2025","journal-title":"Software impacts"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"2911","DOI":"10.3390\/s22082911","article-title":"Brain MRI analysis for Alzheimer\u2019s disease diagnosis using CNN-based feature extraction and machine learning","volume":"22","author":"AlSaeed","year":"2022","journal-title":"Sensors"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"1618550","DOI":"10.3389\/fmed.2025.1618550","article-title":"Transfer deep learning and explainable AI framework for brain tumor and Alzheimer's detection across multiple datasets","volume":"12","author":"Alsubai","year":"2025","journal-title":"Front. Med."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"464","DOI":"10.3390\/make6010024","article-title":"Alzheimer\u2019s disease detection using deep learning on neuroimaging: a systematic review","volume":"6","author":"Alsubaie","year":"2024","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"e70025","DOI":"10.1002\/hsr2.70025","article-title":"Deep learning techniques for Alzheimer's disease detection in 3D imaging: a systematic review","volume":"7","author":"Awang","year":"2024","journal-title":"Health Sci. Rep."},{"key":"ref7","doi-asserted-by":"publisher","first-page":"149","DOI":"10.3390\/biomedicines11010149","article-title":"Hybridized deep learning approach for detecting Alzheimer\u2019s disease","volume":"11","author":"Balaji","year":"2023","journal-title":"Biomedicine"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"2720","DOI":"10.3390\/math12172720","article-title":"Multiple inputs and mixed data for Alzheimer\u2019s disease classification based on 3D vision transformer","volume":"12","author":"Castro-Silva","year":"2024","journal-title":"Mathematics"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"1398","DOI":"10.1109\/ISBI.2018.8363833","volume-title":"2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)","author":"Cui","year":"2018"},{"key":"ref10","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.media.2016.06.032","article-title":"Machine learning approaches in medical image analysis: from detection to diagnosis","volume":"33","author":"De Bruijne","year":"2016","journal-title":"Med. Image Anal."},{"key":"ref11","doi-asserted-by":"publisher","first-page":"118633","DOI":"10.1016\/j.eswa.2022.118633","article-title":"Alzheimer\u2019s disease classification based on graph kernel SVMs constructed with 3D texture features extracted from MR images","volume":"211","author":"de Mendon\u00e7a","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref12","doi-asserted-by":"publisher","first-page":"2501.15293","DOI":"10.48550\/arXiv.2501.15293","article-title":"Deep learning in early Alzheimer's disease's detection: a comprehensive survey of classification, segmentation, and feature extraction methods","author":"Hafeez","year":"2025","journal-title":"arXiv"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s40708-023-00184-w","article-title":"Four-way classification of Alzheimer\u2019s disease using deep Siamese convolutional neural network with triplet-loss function","volume":"10","author":"Hajamohideen","year":"2023","journal-title":"Brain Inform."},{"key":"ref14","doi-asserted-by":"publisher","first-page":"1702902","DOI":"10.3389\/fncom.2025.1702902","article-title":"GAME-net: an ensemble deep learning framework integrating generative autoencoders and attention mechanisms for automated brain tumor segmentation in MRI","volume":"19","author":"Haq","year":"2025","journal-title":"Front. Comput. Neurosci."},{"key":"ref15","first-page":"770","volume-title":"Proceedings of the IEEE Conference on Computer vision and Pattern Recognition","author":"He","year":"2016"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1002\/jmri.21049","article-title":"The Alzheimer\u2019s disease neuroimaging initiative (ADNI): MRI methods","volume":"27","author":"Jack","year":"2008","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"333","DOI":"10.3390\/jimaging10120333","article-title":"Deep learning-based diagnosis algorithm for alzheimer\u2019s disease","volume":"10","author":"Jin","year":"2024","journal-title":"Journal of Imaging"},{"key":"ref18","doi-asserted-by":"publisher","first-page":"853294","DOI":"10.3389\/fpubh.2022.853294","article-title":"Early-stage Alzheimer's disease prediction using machine learning models","volume":"10","author":"Kavitha","year":"2022","journal-title":"Front. Public Health"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"1050777","DOI":"10.3389\/fnins.2022.1050777","article-title":"A transfer learning approach for multiclass classification of Alzheimer's disease using MRI images","volume":"16","author":"Khan","year":"2023","journal-title":"Front. Neurosci."},{"key":"ref20","doi-asserted-by":"publisher","first-page":"106032","DOI":"10.1016\/j.cmpb.2021.106032","article-title":"Alzheimer's disease detection using depthwise separable convolutional neural networks","volume":"203","author":"Liu","year":"2021","journal-title":"Comput. Methods Prog. Biomed."},{"key":"ref21","doi-asserted-by":"publisher","first-page":"1520878","DOI":"10.3389\/fmed.2024.1520878","article-title":"Early detection of Alzheimer\u2019s disease in structural and functional MRI","volume":"11","author":"Maity","year":"2024","journal-title":"Front. Med."},{"key":"ref22","first-page":"574","volume-title":"International Conference on geometric Science of Information","author":"Makaroff","year":"2023"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"79","DOI":"10.4324\/9781003330318-9","volume-title":"Advances in the Neurolinguistic Study of Multilingual and Monolingual Adults","author":"Malcolm","year":"2023"},{"key":"ref24","first-page":"1","volume-title":"2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM)","author":"Manikandan","year":"2023"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"1498","DOI":"10.1162\/jocn.2007.19.9.1498","article-title":"Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults","volume":"19","author":"Marcus","year":"2007","journal-title":"J. Cogn. Neurosci."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"360","DOI":"10.3390\/diagnostics15030360","article-title":"Transfer learning and neural network-based approach on structural MRI data for prediction and classification of Alzheimer\u2019s disease","volume":"15","author":"Momeni","year":"2025","journal-title":"Diagnostics"},{"key":"ref9002","doi-asserted-by":"publisher","first-page":"e62647","DOI":"10.2196\/62647","article-title":"Detection of Alzheimer disease in neuroimages using vision transformers: systematic review and meta-analysis","volume":"27","author":"Mubonanyikuzo","year":"2025","journal-title":"J. Med. Internet Res."},{"key":"ref28","doi-asserted-by":"publisher","first-page":"717","DOI":"10.3390\/diagnostics15060717","article-title":"Multi-modal fusion and longitudinal analysis for Alzheimer\u2019s disease classification using deep learning","volume":"15","author":"Muksimova","year":"2025","journal-title":"Diagnostics"},{"key":"ref9003","doi-asserted-by":"publisher","first-page":"118140","DOI":"10.1016\/j.neuroimage.2021.118140","article-title":"White matter hyperintensities segmentation using the ensemble U-net with multi-scale highlighting foregrounds","volume":"237","author":"Park","year":"2021","journal-title":"NeuroImage"},{"key":"ref30","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1016\/j.neuroimage.2017.03.057","article-title":"A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages","volume":"155","author":"Rathore","year":"2017","journal-title":"NeuroImage"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.jneumeth.2013.11.016","article-title":"Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and progressive Supranuclear palsy","volume":"222","author":"Salvatore","year":"2014","journal-title":"J. Neurosci. Methods"},{"key":"ref32","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1186\/s40708-024-00238-7","article-title":"Ensemble of vision transformer architectures for efficient Alzheimer\u2019s disease classification","volume":"11","author":"Shaffi","year":"2024","journal-title":"Brain Informatics"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"16376","DOI":"10.1109\/access.2023.3244952","article-title":"AlzheimerNet: an effective deep learning based proposition for Alzheimer\u2019s disease stages classification from functional brain changes in magnetic resonance images","volume":"11","author":"Shamrat","year":"2023","journal-title":"IEEE Access"},{"key":"ref34","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1109\/JBHI.2017.2655720","article-title":"Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer's disease","volume":"22","author":"Shi","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref35","first-page":"1556","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014","journal-title":"arXiv"},{"key":"ref36","doi-asserted-by":"publisher","first-page":"101940","DOI":"10.1016\/j.jksuci.2024.101940","article-title":"Classification of Alzheimer\u2019s disease using MRI data based on deep learning techniques","volume":"36","author":"Sorour","year":"2024","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref37","doi-asserted-by":"publisher","first-page":"20250735","DOI":"10.57197\/JDR-2025-0735","article-title":"Comprehensive evaluation of pre-trained neural networks for Alzheimer\u2019s disease classification using transfer learning","volume":"4","author":"Tabish","year":"2025","journal-title":"J. Disability Research"},{"key":"ref38","first-page":"6105","volume-title":"International Conference on Machine Learning","author":"Tan","year":"2019"},{"key":"ref39","doi-asserted-by":"publisher","first-page":"101694","DOI":"10.1016\/j.media.2020.101694","article-title":"Convolutional neural networks for classification of Alzheimer's disease: overview and reproducible evaluation","volume":"63","author":"Wen","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref40","doi-asserted-by":"publisher","first-page":"17037","DOI":"10.1038\/s41598-024-66874-5","article-title":"Exceptional performance with minimal data using a generative adversarial network for alzheimer's disease classification","volume":"14","author":"Wong","year":"2024","journal-title":"Sci. Rep."},{"key":"ref41","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.bspc.2015.05.014","article-title":"Detection of Alzheimer's disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA-KSVM trained by PSOTVAC","volume":"21","author":"Zhang","year":"2015","journal-title":"Biomed. Signal Process. Control"}],"container-title":["Frontiers in Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnins.2026.1871982\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T06:41:06Z","timestamp":1788417666000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnins.2026.1871982\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,3]]},"references-count":41,"alternative-id":["10.3389\/fnins.2026.1871982"],"URL":"https:\/\/doi.org\/10.3389\/fnins.2026.1871982","relation":{},"ISSN":["1662-453X"],"issn-type":[{"value":"1662-453X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,3]]},"article-number":"1871982"}}