{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T04:17:18Z","timestamp":1776226638862,"version":"3.50.1"},"reference-count":42,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100017542","name":"Beijing Hospital","doi-asserted-by":"publisher","award":["2022BJYYEC-375-01"],"award-info":[{"award-number":["2022BJYYEC-375-01"]}],"id":[{"id":"10.13039\/501100017542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62271177"],"award-info":[{"award-number":["62271177"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Zhejiang Province Natural Science Foundation","doi-asserted-by":"publisher","award":["LZ24F010007"],"award-info":[{"award-number":["LZ24F010007"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014041","name":"Alzheimer's Disease Neuroimaging Initiative","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100014041","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003467","name":"Hangzhou Dianzi University","doi-asserted-by":"publisher","award":["IRB-2020001"],"award-info":[{"award-number":["IRB-2020001"]}],"id":[{"id":"10.13039\/501100003467","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.bspc.2026.110183","type":"journal-article","created":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T02:06:14Z","timestamp":1775181974000},"page":"110183","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PC","title":["A novel lightweight multi-attention fusion network for Alzheimer\u2019s disease detection"],"prefix":"10.1016","volume":"120","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7662-9126","authenticated-orcid":false,"given":"Jinhua","family":"Sheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruilin","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miao","family":"Miao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.bspc.2026.110183_b0005","unstructured":"World Health Organization. Dementia [EB\/OL]. 2023-03-15 [2023-07-10]."},{"key":"10.1016\/j.bspc.2026.110183_b0010","unstructured":"National Institute on Aging. Alzheimer's Disease Fact Sheet [EB\/OL]. Archived 2022-03-23 [Retrieved 2022-03-23]."},{"issue":"5","key":"10.1016\/j.bspc.2026.110183_b0015","doi-asserted-by":"crossref","first-page":"3708","DOI":"10.1002\/alz.13809","article-title":"2024 Alzheimer's disease facts and figures","volume":"20","author":"Association","year":"2024","journal-title":"Alzheimer\u2019s & Dementia"},{"key":"10.1016\/j.bspc.2026.110183_b0020","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/1471-2318-7-18","article-title":"Systematic review of information and support interventions for caregivers of people with dementia","volume":"7","author":"Bunn","year":"2007","journal-title":"BMC Geriatr."},{"issue":"11","key":"10.1016\/j.bspc.2026.110183_b0025","doi-asserted-by":"crossref","first-page":"1106","DOI":"10.2174\/1570159X18666200528142429","article-title":"Recent advancements in pathogenesis, diagnostics and treatment of Alzheimer's disease","volume":"18","author":"Khan","year":"2020","journal-title":"Curr. Neuropharmacol."},{"key":"10.1016\/j.bspc.2026.110183_b0030","doi-asserted-by":"crossref","DOI":"10.1093\/jnen\/nlaa014","article-title":"Concordance of clinical Alzheimer diagnosis and neuropathological features at autopsy","volume":"79","author":"Gauthreaux","year":"2020","journal-title":"J. Neuropathol. Exp. Neurol."},{"key":"10.1016\/j.bspc.2026.110183_b0035","unstructured":"National Institute for Health and Clinical Excellence. Dementia: Quick Reference Guide. London: NICE, 2006."},{"key":"10.1016\/j.bspc.2026.110183_b0040","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.neuroscience.2025.04.035","article-title":"A review of multimodal fusion-based deep learning for Alzheimer\u2019s disease","volume":"576","author":"Zhang","year":"2025","journal-title":"Neuroscience"},{"issue":"7553","key":"10.1016\/j.bspc.2026.110183_b0045","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"issue":"11","key":"10.1016\/j.bspc.2026.110183_b0050","first-page":"3325","article-title":"Deep learning on computational-resource-limited platforms: a survey","volume":"39","author":"Chen","year":"2020","journal-title":"IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst."},{"key":"10.1016\/j.bspc.2026.110183_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2020.102362","article-title":"Early diagnosis model of Alzheimer\u2019s disease based on sparse logistic regression with the generalized elastic net","volume":"66","author":"Xiao","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"issue":"6","key":"10.1016\/j.bspc.2026.110183_b0060","doi-asserted-by":"crossref","first-page":"2688","DOI":"10.1016\/j.jksuci.2020.04.004","article-title":"An improved multi-modal based machine learning approach for the prognosis of Alzheimer\u2019s disease","volume":"34","author":"Khan","year":"2022","journal-title":"J. King Saud Univ. \u2013 Comput. Info. Sci."},{"key":"10.1016\/j.bspc.2026.110183_b0065","article-title":"Leung-Malik features and Adaboost perform classification of Alzheimer\u2019s disease stages","author":"Basheera","year":"2022","journal-title":"IETE J. Res."},{"key":"10.1016\/j.bspc.2026.110183_b0070","doi-asserted-by":"crossref","DOI":"10.3389\/fnagi.2022.754334","article-title":"Classification of Alzheimer\u2019s disease based on abnormal hippocampal functional connectivity and machine learning","volume":"14","author":"Zhu","year":"2022","journal-title":"Front. Aging Neurosci."},{"issue":"3","key":"10.1016\/j.bspc.2026.110183_b0075","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.1109\/JBHI.2021.3113668","article-title":"Regression and classification of Alzheimer\u2019s disease diagnosis using NMF-TDNet features from 3D brain MR image","volume":"26","author":"Lao","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.bspc.2026.110183_b0080","doi-asserted-by":"crossref","unstructured":"\u00d6z\u00e7elik Y B, Altan A, Kaya C. Machine learning approach for early diagnosis of alzheimer's disease using rs-fMRI and metaheuristic optimization with functional connectivity matrices. In: 2024 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), 2024: 248\u2013253. doi:10.1109\/IICAIET62352.2024.10730420.","DOI":"10.1109\/IICAIET62352.2024.10730420"},{"issue":"12","key":"10.1016\/j.bspc.2026.110183_b0085","doi-asserted-by":"crossref","first-page":"4182","DOI":"10.3390\/s21124182","article-title":"An improved deep residual network prediction model for the early diagnosis of Alzheimer\u2019s disease","volume":"21","author":"Sun","year":"2021","journal-title":"Sensors"},{"key":"10.1016\/j.bspc.2026.110183_b0090","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/s12021-023-09646-2","article-title":"A deep learning-based ensemble method for early diagnosis of Alzheimer\u2019s disease using MRI images","volume":"22","author":"Fathi","year":"2024","journal-title":"Neuroinformatics"},{"key":"10.1016\/j.bspc.2026.110183_b0095","series-title":"In: Proceedings of the IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)","first-page":"133","article-title":"Transfer Learning for Alzheimer's Disease Detection on MRI Images","author":"Ebrahimi-Ghahnavieh","year":"2019"},{"key":"10.1016\/j.bspc.2026.110183_b0100","doi-asserted-by":"crossref","DOI":"10.1007\/978-981-16-5188-5","article-title":"A new deep belief network-based multi-task learning for diagnosis of Alzheimer\u2019s disease","author":"Zeng","year":"2021","journal-title":"Neural Comput. Applic."},{"issue":"20","key":"10.1016\/j.bspc.2026.110183_b0105","doi-asserted-by":"crossref","first-page":"13587","DOI":"10.1007\/s00521-021-05983-y","article-title":"U-net based analysis of MRI for Alzheimer\u2019s disease diagnosis","volume":"33","author":"Fan","year":"2021","journal-title":"Neural Comput. Applic."},{"issue":"1","key":"10.1016\/j.bspc.2026.110183_b0110","first-page":"9","article-title":"Predict Alzheimer\u2019s disease using hippocampus MRI data: a lightweight 3D deep convolutional network model with visual and global shape representations","volume":"13","author":"Katabathula","year":"2021","journal-title":"Alzheimer\u2019s Res. Therapy"},{"issue":"9","key":"10.1016\/j.bspc.2026.110183_b0115","doi-asserted-by":"crossref","first-page":"4945","DOI":"10.1109\/TNNLS.2021.3063516","article-title":"Tensorizing GAN with high-order pooling for Alzheimer\u2019s disease assessment","volume":"33","author":"Yu","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"2","key":"10.1016\/j.bspc.2026.110183_b0120","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1002\/hbm.25685","article-title":"A parallel attention-augmented bilinear network for early magnetic resonance imaging-based diagnosis of Alzheimer\u2019s disease","volume":"43","author":"Guan","year":"2022","journal-title":"Hum. Brain Mapp."},{"key":"10.1016\/j.bspc.2026.110183_b0125","doi-asserted-by":"crossref","unstructured":"Kina E. TLEABLCNN: Brain and Alzheimer\u2019s Disease Detection Using Attention-Based Explainable Deep Learning and SMOTE Using Imbalanced Brain MRI [J]. In: IEEE Access, 2025, 13: 27670\u201327683. doi: 10.1109\/ACCESS.2025.3539550.","DOI":"10.1109\/ACCESS.2025.3539550"},{"key":"10.1016\/j.bspc.2026.110183_b0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.109438","article-title":"Multi-scale multimodal deep learning framework for Alzheimer's disease diagnosis","volume":"184","author":"Abdelaziz","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.bspc.2026.110183_b0135","doi-asserted-by":"crossref","unstructured":"Hu Z, Wang Z, Jin Y, Hou W. VGG-TSwinformer: Transformer-based deep learning model for early Alzheimer\u2019s disease prediction. In: Computer Methods and Programs in Biomedicine, 2023, 229: 107291. doi: 10.1016\/j.cmpb.2022.107291.","DOI":"10.1016\/j.cmpb.2022.107291"},{"key":"10.1016\/j.bspc.2026.110183_b0140","doi-asserted-by":"crossref","unstructured":"Wang A, Chen H, Lin Z, Han J, Ding G. LSNet: See Large, Focus Small. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025: 9718\u20139729.","DOI":"10.1109\/CVPR52734.2025.00908"},{"key":"10.1016\/j.bspc.2026.110183_b0145","series-title":"In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","article-title":"ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks","author":"Wang","year":"2020"},{"issue":"5","key":"10.1016\/j.bspc.2026.110183_b0150","doi-asserted-by":"crossref","first-page":"809","DOI":"10.3174\/ajnr.A2061","article-title":"Texture analysis: a review of neurologic MR imaging applications","volume":"31","author":"Kassner","year":"2010","journal-title":"Am. J. Neuroradiol."},{"key":"10.1016\/j.bspc.2026.110183_b0155","series-title":"In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"2881","article-title":"Pyramid Scene Parsing Network [C]","author":"Zhao","year":"2017"},{"issue":"4","key":"10.1016\/j.bspc.2026.110183_b0160","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.bspc.2026.110183_b0165","series-title":"In: Proceedings of the European Conference on Computer Vision (ECCV)","first-page":"3","article-title":"CBAM: Convolutional Block Attention Module","author":"Woo","year":"2018"},{"key":"10.1016\/j.bspc.2026.110183_b0170","series-title":"In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","article-title":"Deep Residual Learning for image Recognition","author":"He","year":"2016"},{"key":"10.1016\/j.bspc.2026.110183_b0175","series-title":"In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","article-title":"Squeeze-and-Excitation Networks","author":"Hu","year":"2018"},{"key":"10.1016\/j.bspc.2026.110183_b0180","series-title":"In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","article-title":"Rethinking the Inception Architecture for Computer Vision","author":"Szegedy","year":"2016"},{"key":"10.1016\/j.bspc.2026.110183_b0185","doi-asserted-by":"crossref","unstructured":"Ma X, Dai X, Bai Y, Wang Y, Fu Y. Rewrite the Stars. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024: 5694\u20135703.","DOI":"10.1109\/CVPR52733.2024.00544"},{"key":"10.1016\/j.bspc.2026.110183_b0190","unstructured":"Tan M, Le Q. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In: Proceedings of the 36th International Conference on Machine Learning (ICML), 2019: 6105\u20136114."},{"key":"10.1016\/j.bspc.2026.110183_b0195","series-title":"In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"4700","article-title":"Densely Connected Convolutional Networks","author":"Huang","year":"2017"},{"key":"10.1016\/j.bspc.2026.110183_b0200","doi-asserted-by":"crossref","unstructured":"Chen Z, Xie L, Niu J, Liu X, Wei L, Tian Q. Visformer: The Vision-Friendly Transformer. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), 2021: 589\u2013598.","DOI":"10.1109\/ICCV48922.2021.00063"},{"key":"10.1016\/j.bspc.2026.110183_b0205","doi-asserted-by":"crossref","first-page":"11258","DOI":"10.1038\/s41598-018-29295-9","article-title":"Structural brain imaging in Alzheimer\u2019s disease and mild cognitive impairment: biomarker analysis and shared morphometry database","volume":"8","author":"Ledig","year":"2018","journal-title":"Sci. Rep."},{"issue":"4","key":"10.1016\/j.bspc.2026.110183_b0210","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1002\/hbm.20160","article-title":"Altered resting state networks in mild cognitive impairment and mild Alzheimer's disease: an fMRI study","volume":"26","author":"Rombouts","year":"2005","journal-title":"Hum. Brain Mapp."}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426007378?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426007378?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T03:37:09Z","timestamp":1776224229000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426007378"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":42,"alternative-id":["S1746809426007378"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110183","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A novel lightweight multi-attention fusion network for Alzheimer\u2019s disease detection","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110183","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"110183"}}