{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:53:18Z","timestamp":1792788798427,"version":"4.5.0"},"reference-count":27,"publisher":"Public Library of Science (PLoS)","issue":"1","license":[{"start":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T00:00:00Z","timestamp":1737936000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Langfang Bureau of Science and Technology, Hebei Province, China","award":["2024011019"],"award-info":[{"award-number":["2024011019"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>With the increasing number of patients with Alzheimer\u2019s Disease (AD), the demand for early diagnosis and intervention is becoming increasingly urgent. The traditional detection methods for Alzheimer\u2019s disease mainly rely on clinical symptoms, biomarkers, and imaging examinations. However, these methods have limitations in the early detection of Alzheimer\u2019s disease, such as strong subjectivity in diagnostic criteria, high detection costs, and high misdiagnosis rates. To address these issues, this study proposes a deep learning model to detect Alzheimer\u2019s disease; it is called Enhanced Residual Attention Network (ERAN) that can classify medical images. By combining residual learning, attention mechanism, and soft thresholding, the feature representation ability and classification accuracy of the model have been improved. The accuracy of the model in detecting Alzheimer\u2019s disease has reached 99.36%, with a loss rate of only 0.0264. The experimental results indicate that the Enhanced Residual Attention Network has achieved excellent performance on the Alzheimer\u2019s disease test dataset, providing strong support for the early diagnosis and treatment of Alzheimer\u2019s disease.<\/jats:p>","DOI":"10.1371\/journal.pone.0317376","type":"journal-article","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T13:39:08Z","timestamp":1737985148000},"page":"e0317376","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":18,"title":["Alzheimer\u2019s disease image classification based on enhanced residual attention network"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-2820-6354","authenticated-orcid":true,"given":"Xiaoli","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bairui","family":"Gong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinfang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoming","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,1,27]]},"reference":[{"issue":"04","key":"pone.0317376.ref001","first-page":"417","article-title":"Research progress in the diagnosis of Alzheimer\u2019s disease [J].","volume":"34","author":"Hou Weili","year":"2020","journal-title":"Chinese Journal of Practical Diagnosis and Treatment"},{"issue":"3","key":"pone.0317376.ref002","first-page":"379","article-title":"Classification and prediction of Alzheimer\u2019s disease based on machine learning [J]","volume":"37","author":"Li Cai","year":"2020","journal-title":"Chinese Journal of Medical Physics"},{"key":"pone.0317376.ref003","article-title":"Lattice 123 pattern for automated Alzheimer\u2019s detection using EEG signal.","author":"S. Dogan","year":"2024","journal-title":"Cogn Neurodyn"},{"key":"pone.0317376.ref004","first-page":"1","article-title":"Primate brain pattern-based automated Alzheimer\u2019s disease detection model using EEG signals[J].","author":"S Dogan","year":"2022","journal-title":"Cognitive Neurodynamics"},{"issue":"4","key":"pone.0317376.ref005","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.neurobiolaging.2006.11.010","article-title":"Detection of prodromal Alzheimer\u2019s disease via pattern classification of magnetic resonance imaging","volume":"29","author":"C Davatzikos","year":"2008","journal-title":"Neurobiol Aging"},{"key":"pone.0317376.ref006","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.media.2017.01.008","article-title":"Alzheimer\u2019s Disease Neuroimaging Initiative. Deep ensemble learning of sparse regression models for brain disease diagnosis","volume":"37","author":"HI Suk","year":"2017","journal-title":"Med Image Anal"},{"key":"pone.0317376.ref007","volume-title":"Tang Vanadium Research on postoperative glioma CT image segmentation method based on deep learning","year":"2019"},{"key":"pone.0317376.ref008","doi-asserted-by":"crossref","first-page":"908143","DOI":"10.3389\/fnagi.2022.908143","article-title":"Soft Attention Based DenseNet Model for Parkinson\u2019s Disease Classification Using SPECT Images.","volume":"14","author":"M Thakur","year":"2022","journal-title":"Front Aging Neurosci."},{"key":"pone.0317376.ref009","first-page":"167","volume-title":"Jiawei Xiang. Improved deep convolution neural network (CNN) for the identification of defects in the centrifugal pump using acoustic images[J].","author":"Anil Kumar","year":"2020"},{"key":"pone.0317376.ref010","first-page":"144","volume-title":"CNN-LSTM deep learning architecture for computer vision-based modal frequency detection","author":"Ruoyu Yang","year":"2020"},{"key":"pone.0317376.ref011","first-page":"1","article-title":"A Novel Centralized Federated Deep Fuzzy Neural Network with Multi-objectives Neural Architecture Search for Epistatic Detection","author":"Xiang Wu","year":"2024","journal-title":"IEEE Transactions on Fuzzy Systems"},{"issue":"7","key":"pone.0317376.ref012","doi-asserted-by":"crossref","first-page":"4681","DOI":"10.1109\/TII.2019.2943898","article-title":"Deep residual shrinkage networks for fault diagnosis[J]","volume":"16","author":"M. Zhao","year":"2020","journal-title":"IEEE Transactions on Industrial Informatics"},{"issue":"06","key":"pone.0317376.ref013","first-page":"1068","article-title":"Research on Alzheimer\u2019s Disease Classification Algorithm Based on 3D ResNet [J]","volume":"42","author":"Yu Song","year":"2020","journal-title":"Computer Engineering and Science"},{"issue":"06","key":"pone.0317376.ref014","first-page":"56","article-title":"Dong Wenfei Alzheimer\u2019s disease image classification based on improved EfficientNet [J]","author":"Zhu Jianbo","year":"2023","journal-title":"Computer and Modernization"},{"issue":"3","key":"pone.0317376.ref015","doi-asserted-by":"crossref","first-page":"319","DOI":"10.3390\/brainsci12030319","article-title":"Image Classification of Alzheimer\u2019s Disease Based on External-Attention Mechanism and Fully Convolutional Network","volume":"12","author":"M Jiang","year":"2022","journal-title":"Brain Sci"},{"issue":"03","key":"pone.0317376.ref016","article-title":"Alzheimer\u2019s Disease Classification Network Based on Improved ResNet [J]","volume":"13","author":"Wang Bin","year":"2023","journal-title":"Intelligent Computers and Applications"},{"key":"pone.0317376.ref017","doi-asserted-by":"crossref","first-page":"104828","DOI":"10.1016\/j.compbiomed.2021.104828","article-title":"Feed-forward LPQNet based Automatic Alzheimer\u2019s Disease Detection Model[J]","volume":"137","author":"E Kaplan","year":"2021","journal-title":"Computers in biology and medicine"},{"key":"pone.0317376.ref018","doi-asserted-by":"crossref","first-page":"1038636","DOI":"10.3389\/fncom.2023.1038636","article-title":"Conventional machine learning and deep learning in Alzheimer\u2019s disease diagnosis using neuroimaging: A review[J]","volume":"17","author":"Z Zhao","year":"2023","journal-title":"Frontiers in computational neuroscience"},{"issue":"5","key":"pone.0317376.ref019","first-page":"2417","article-title":"Recent diagnostic techniques for the detection of Alzheimer\u2019s disease: a short review[J]","volume":"2023192","author":"S Bhujbal S","year":"1971","journal-title":"Irish Journal of Medical Science"},{"issue":"15","key":"pone.0317376.ref020","doi-asserted-by":"crossref","first-page":"2965","DOI":"10.3390\/rs13152965","article-title":"Effect of attention mechanism in deep learning-based remote sensing image processing: A systematic literature review[J].","volume":"13","author":"S Ghaffarian","year":"2021","journal-title":"Remote Sensing,"},{"key":"pone.0317376.ref021","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.neucom.2021.03.091","article-title":"A review on the attention mechanism of deep learning[J].","volume":"452","author":"Z Niu","year":"2021","journal-title":"Neurocomputing"},{"key":"pone.0317376.ref022","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 7132\u20137141.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"pone.0317376.ref023","doi-asserted-by":"crossref","unstructured":"Zhang Y, Li K, Li K, et al. Image super-resolution using very deep residual channel attention networks[C]\/\/Proceedings of the European conference on computer vision (ECCV). 2018: 286\u2013301.","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"pone.0317376.ref024","doi-asserted-by":"crossref","unstructured":"Wang F, Jiang M, Qian C, et al. Residual attention network for image classification[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 3156\u20133164.","DOI":"10.1109\/CVPR.2017.683"},{"key":"pone.0317376.ref025","doi-asserted-by":"crossref","unstructured":"P. Sun et al., \"RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection,\" 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 2021, pp. 5721\u20135730.","DOI":"10.1109\/CVPR46437.2021.00567"},{"key":"pone.0317376.ref026","doi-asserted-by":"crossref","unstructured":"Shi Z, Chen C, Xiong Z, et al. Deep residual attention network for spectral image super-resolution[C]\/\/Proceedings of the European Conference on Computer Vision (ECCV) Workshops. 2018: 0\u20130.","DOI":"10.1007\/978-3-030-11021-5_14"},{"issue":"12","key":"pone.0317376.ref027","doi-asserted-by":"crossref","first-page":"2192","DOI":"10.3390\/math8122192","article-title":"Learning medical image denoising with deep dynamic residual attention network[J].","volume":"8","author":"A Sharif S M","year":"2020","journal-title":"Mathematics"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0317376","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T13:39:36Z","timestamp":1737985176000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0317376"}},"subtitle":[],"editor":[{"given":"Ian James","family":"Martins","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2025,1,27]]},"references-count":27,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1,27]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0317376","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,27]]}}}