{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T15:02:45Z","timestamp":1784300565475,"version":"3.55.0"},"reference-count":43,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T00:00:00Z","timestamp":1782432000000},"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. Aging Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Alzheimer's disease (AD) is a progressive neurodegenerative disorder that requires accurate and early diagnosis. Deep learning methods have shown significant potential for automated MRI-based AD classification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A hybrid deep learning framework integrating ResNet152V2, Convolutional Block Attention Module (CBAM), and Bidirectional Gated Recurrent Unit (Bi-GRU) was developed for three-class classification of Normal Cognition (NC), Mild Cognitive Impairment (MCI), and Alzheimer's Disease (AD). The model was trained using 2,100 ADNI subjects and externally validated using 900 OASIS subjects.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The proposed framework achieved 94.5% classification accuracy with an AUC of 95.0% on the ADNI dataset and 92.8% accuracy on the OASIS dataset. Comparative analyses demonstrated improvements of 5.2\u20137.0% over baseline models. Ablation studies confirmed the contribution of CBAM and Bi-GRU to overall performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The integration of deep residual feature extraction, attention-based refinement, and sequential modeling effectively captures disease-related anatomical patterns. The results suggest that the proposed framework may support automated multi-stage Alzheimer's disease classification and provide a foundation for future computer-aided diagnostic systems.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnagi.2026.1798861","type":"journal-article","created":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T05:46:39Z","timestamp":1782452799000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A deep residual attention\u2013Recurrent model for early and multi-stage Alzheimer's disease detection"],"prefix":"10.3389","volume":"18","author":[{"given":"Uma Maheswara Rao","family":"Munipalli","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, VIT-AP University","place":["Amaravathi, Andhra Pradesh, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Visalakshi","family":"Annepu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, VIT-AP University","place":["Amaravathi, Andhra Pradesh, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,26]]},"reference":[{"key":"B1","first-page":"1","article-title":"\u201cDeep learning for Alzheimer's diagnosis: ResNet152V2 approach on MRI dataset,\u201d","volume-title":"Proceedings of the 2024 1st International Conference on Cognitive, Green and Ubiquitous Computing (IC-CGU)","author":"Adjei","year":"2024"},{"key":"B2","doi-asserted-by":"publisher","first-page":"i49","DOI":"10.1093\/bioinformatics\/btad249","article-title":"PPAD: a deep learning architecture to predict progression of Alzheimer's disease","volume":"39","author":"Al Olaimat","year":"2023","journal-title":"Bioinformatics"},{"key":"B3","doi-asserted-by":"publisher","first-page":"23011","DOI":"10.1038\/s41598-025-05529-5","article-title":"A novel neuroimaging based early detection framework for Alzheimer disease using deep learning","volume":"15","author":"Alasiry","year":"2025","journal-title":"Sci. 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