{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T05:06:30Z","timestamp":1791522390580,"version":"4.3.4"},"reference-count":45,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T00:00:00Z","timestamp":1755475200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100020912","name":"King Faisal University","doi-asserted-by":"publisher","award":["KFU252479"],"award-info":[{"award-number":["KFU252479"]}],"id":[{"id":"10.13039\/501100020912","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Alzheimer\u2019s disease (AD) poses significant challenges to healthcare systems across the globe. Early and accurate AD diagnosis is crucial for effective management and treatment. Recent advances in neuroimaging and genomics provide an opportunity for developing multi-modality-based AD diagnosis models using artificial intelligence (AI) techniques. However, the data complexities cause challenges in developing interpretable AI-based AD identification models. In this study, the author built a comprehensive AD diagnostic model using magnetic resonance imaging (MRI) and gene expression data. MobileNet V3 and EfficientNet B7 model was employed to extract AD features from gene expression data. The author introduced a hybrid TWIN-Performer-based feature extraction model to derive features from MRI. The attention-based feature fusion was used to fuse the crucial features. An ensemble learning-based classification model integrating CatBoost, XGBoost, and extremely randomized tree (ERT) was developed to identify cognitively normal (CN) and AD features. The proposed model was validated on diverse datasets. It achieved a superior performance on MRI and gene expression datasets. The area under the receiver operating characteristic (AUROC) scores were consistently above 0.85, indicating excellent model performance. The use of Shapley Additive exPlanations (SHAP) values improved the model\u2019s interpretability, leading to earlier interventions and personalized treatment strategies.<\/jats:p>","DOI":"10.1371\/journal.pone.0330085","type":"journal-article","created":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T17:31:57Z","timestamp":1755538317000},"page":"e0330085","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["Deep learning-based Alzheimer\u2019s disease detection using magnetic resonance imaging and gene expression data"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0960-3727","authenticated-orcid":true,"given":"Badar","family":"Almarri","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,8,18]]},"reference":[{"issue":"1","key":"pone.0330085.ref001","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1186\/s13024-019-0333-5","article-title":"The neuropathological diagnosis of Alzheimer\u2019s disease","volume":"14","author":"MA DeTure","year":"2019","journal-title":"Mol Neurodegener"},{"issue":"6","key":"pone.0330085.ref002","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1016\/S1474-4422(21)00066-1","article-title":"Clinical diagnosis of Alzheimer\u2019s disease: recommendations of the International Working Group","volume":"20","author":"B Dubois","year":"2021","journal-title":"Lancet Neurol"},{"issue":"3","key":"pone.0330085.ref003","doi-asserted-by":"crossref","first-page":"371","DOI":"10.14283\/jpad.2021.23","article-title":"Diagnosis of Early Alzheimer\u2019s Disease: Clinical Practice in 2021","volume":"8","author":"AP Porsteinsson","year":"2021","journal-title":"J Prev Alzheimers Dis"},{"key":"pone.0330085.ref004","doi-asserted-by":"crossref","first-page":"101544","DOI":"10.1016\/j.arr.2021.101544","article-title":"Biomarkers used in Alzheimer\u2019s disease diagnosis, treatment, and prevention","volume":"74","author":"YAR Mahaman","year":"2022","journal-title":"Ageing Res Rev"},{"issue":"1","key":"pone.0330085.ref005","article-title":"Clinical application of CSF biomarkers for Alzheimer\u2019s disease: From rationale to ratios","volume":"14","author":"FH Bouwman","year":"2022","journal-title":"Alzheimers Dement (Amst)"},{"issue":"8","key":"pone.0330085.ref006","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1038\/s43587-022-00269-x","article-title":"Designing the next-generation clinical care pathway for Alzheimer\u2019s disease","volume":"2","author":"H Hampel","year":"2022","journal-title":"Nat Aging"},{"issue":"1","key":"pone.0330085.ref007","article-title":"Current advances in digital cognitive assessment for preclinical Alzheimer\u2019s disease","volume":"13","author":"F \u00d6hman","year":"2021","journal-title":"Alzheimers Dement (Amst)"},{"issue":"11","key":"pone.0330085.ref008","doi-asserted-by":"crossref","first-page":"6079","DOI":"10.3390\/ijms23116079","article-title":"Neuroimaging Modalities in Alzheimer\u2019s Disease: Diagnosis and Clinical Features","volume":"23","author":"J Kim","year":"2022","journal-title":"Int J Mol Sci"},{"key":"pone.0330085.ref009","doi-asserted-by":"crossref","first-page":"105622","DOI":"10.1016\/j.compbiomed.2022.105622","article-title":"Early detection of Alzheimer\u2019s disease using single nucleotide polymorphisms analysis based on gradient boosting tree","volume":"146","author":"H Ahmed","year":"2022","journal-title":"Comput Biol Med"},{"key":"pone.0330085.ref010","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","volume":"17","author":"Z Zhao","year":"2023","journal-title":"Front Comput Neurosci"},{"issue":"4","key":"pone.0330085.ref011","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1007\/s00062-021-01057-7","article-title":"Systematic Review of Different Neuroimaging Correlates in Mild Cognitive Impairment and Alzheimer\u2019s Disease","volume":"31","author":"P Talwar","year":"2021","journal-title":"Clin Neuroradiol"},{"key":"pone.0330085.ref012","doi-asserted-by":"crossref","first-page":"102332","DOI":"10.1016\/j.artmed.2022.102332","article-title":"Deep learning for Alzheimer\u2019s disease diagnosis: A survey","volume":"130","author":"M Khojaste-Sarakhsi","year":"2022","journal-title":"Artif Intell Med"},{"key":"pone.0330085.ref013","doi-asserted-by":"crossref","first-page":"105634","DOI":"10.1016\/j.compbiomed.2022.105634","article-title":"Early diagnosis of Alzheimer\u2019s disease based on deep learning: A systematic review","volume":"146","author":"S Fathi","year":"2022","journal-title":"Comput Biol Med"},{"issue":"5","key":"pone.0330085.ref014","doi-asserted-by":"crossref","first-page":"1711","DOI":"10.1007\/s12559-021-09946-2","article-title":"Deep Learning Approach for Early Detection of Alzheimer\u2019s Disease","volume":"14","author":"HA Helaly","year":"2022","journal-title":"Cognit Comput"},{"issue":"11","key":"pone.0330085.ref015","article-title":"A deep learning model for detection of Alzheimer\u2019s disease based on retinal photographs: a retrospective, multicentre case-control study","volume":"4","author":"CY Cheung","year":"2022","journal-title":"Lancet Digit Health"},{"issue":"1","key":"pone.0330085.ref016","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1186\/s13195-022-01131-3","article-title":"Deep learning-based speech analysis for Alzheimer\u2019s disease detection: a literature review","volume":"14","author":"Q Yang","year":"2022","journal-title":"Alzheimers Res Ther"},{"key":"pone.0330085.ref017","doi-asserted-by":"crossref","first-page":"105032","DOI":"10.1016\/j.compbiomed.2021.105032","article-title":"Deep learning based pipelines for Alzheimer\u2019s disease diagnosis: A comparative study and a novel deep-ensemble method","volume":"141","author":"A Loddo","year":"2022","journal-title":"Comput Biol Med"},{"key":"pone.0330085.ref018","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ijcce.2021.12.002","article-title":"A review of the application of deep learning in the detection of Alzheimer\u2019s disease","volume":"3","author":"S Gao","year":"2022","journal-title":"International Journal of Cognitive Computing in Engineering"},{"issue":"1","key":"pone.0330085.ref019","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1111\/jon.13063","article-title":"Functional magnetic resonance imaging, deep learning, and Alzheimer\u2019s disease: A systematic review","volume":"33","author":"SL Warren","year":"2023","journal-title":"J Neuroimaging"},{"key":"pone.0330085.ref020","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.neuroscience.2022.03.026","article-title":"A Single Model Deep Learning Approach for Alzheimer\u2019s Disease Diagnosis","volume":"491","author":"F Zhang","year":"2022","journal-title":"Neuroscience"},{"key":"pone.0330085.ref021","article-title":"Fuzzy Deep Learning for the Diagnosis of Alzheimer\u2019s Disease: Approaches and Challenges","author":"M Tanveer","year":"2024","journal-title":"IEEE Transactions on Fuzzy Systems"},{"key":"pone.0330085.ref022","doi-asserted-by":"crossref","unstructured":"Chaihtra DH, Shetty SV. Alzheimer\u2019s disease detection from brain MRI data using deep learning techniques. In: 2021 2nd Global Conference for Advancement in Technology (GCAT), 2021. 1\u20135.","DOI":"10.1109\/GCAT52182.2021.9587756"},{"key":"pone.0330085.ref023","doi-asserted-by":"crossref","unstructured":"Jagadeeswari M, Priya SP, Athira K, Dhanalakshmi M, Shree PG. Prediction and classification of Alzheimer\u2019s disease using deep learning. In: 2022 3rd International Conference on Electronics and Sustainable Communication Systems (ICESC), 2022. 834\u20139.","DOI":"10.1109\/ICESC54411.2022.9885594"},{"key":"pone.0330085.ref024","doi-asserted-by":"crossref","unstructured":"Singh R, Prabha C, Dixit HM, Kumari S. Alzheimer Disease Detection using Deep Learning. In: 2023 International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS), 2023. 01\u20136. http:\/\/dx.doi.org\/10.1109\/icssas57918.2023.10331661 doi: 10.1109\/icssas57918.2023.10331661","DOI":"10.1109\/ICSSAS57918.2023.10331661"},{"issue":"21","key":"pone.0330085.ref025","doi-asserted-by":"crossref","first-page":"2363","DOI":"10.3390\/diagnostics14212363","article-title":"A Feature-Fusion Technique-Based Alzheimer\u2019s Disease Classification Using Magnetic Resonance Imaging","volume":"14","author":"ARW Sait","year":"2024","journal-title":"Diagnostics (Basel)"},{"key":"pone.0330085.ref026","article-title":"Pvtad: alzheimer\u2019s disease diagnosis using pyramid vision transformer applied to white matter of T1-Weighted structural MRI data","volume":"2024","author":"Alzheimer\u2019s Disease Neuroimaging Initiative","year":"2024","journal-title":"Proc IEEE Int Symp Biomed Imaging"},{"key":"pone.0330085.ref027","doi-asserted-by":"crossref","DOI":"10.1007\/s10796-024-10541-7","article-title":"FGI-CogViT: Fuzzy granule-based interpretable cognitive vision transformer for early detection of Alzheimer\u2019s disease using MRI scan images","volume-title":"Information Systems Frontiers","author":"A Pramanik","year":"2024"},{"key":"pone.0330085.ref028","doi-asserted-by":"crossref","unstructured":"Khatri U, Shin S, Kwon GR. Convolution Driven Vision Transformer for the Prediction of Mild Cognitive Impairment to Alzheimer\u2019s Disease Progression. In: Proceedings of the 2024 IEEE International Conference on Consumer Electronics (ICCE). Las Vegas, NV, USA; 2024. 1\u20137.","DOI":"10.1109\/ICCE59016.2024.10444149"},{"issue":"1","key":"pone.0330085.ref029","doi-asserted-by":"crossref","first-page":"17394","DOI":"10.1038\/s41598-018-35789-3","article-title":"Systematic Analysis and Biomarker Study for Alzheimer\u2019s Disease","volume":"8","author":"X Li","year":"2018","journal-title":"Sci Rep"},{"issue":"1","key":"pone.0330085.ref030","doi-asserted-by":"crossref","first-page":"3485","DOI":"10.1038\/s41598-020-60595-1","article-title":"Prediction of Alzheimer\u2019s disease using blood gene expression data","volume":"10","author":"T Lee","year":"2020","journal-title":"Sci Rep"},{"key":"pone.0330085.ref031","doi-asserted-by":"crossref","first-page":"846638","DOI":"10.3389\/fnins.2022.846638","article-title":"Alzheimer\u2019s disease classification through imaging genetic data with IGnet","volume":"16","author":"JX Wang","year":"2022","journal-title":"Front Neuroscience"},{"issue":"12","key":"pone.0330085.ref032","doi-asserted-by":"crossref","first-page":"3304","DOI":"10.3390\/biomedicines11123304","article-title":"Deep Learning-Based Prediction of Alzheimer\u2019s Disease Using Microarray Gene Expression Data","volume":"11","author":"MM Abdelwahab","year":"2023","journal-title":"Biomedicines"},{"issue":"1","key":"pone.0330085.ref033","doi-asserted-by":"crossref","first-page":"3254","DOI":"10.1038\/s41598-020-74399-w","article-title":"Multimodal deep learning models for early detection of Alzheimer\u2019s disease stage","volume":"11","author":"J Venugopalan","year":"2021","journal-title":"Sci Rep"},{"issue":"1","key":"pone.0330085.ref034","article-title":"MRI-based Deep Learning Assessment of Amyloid, Tau, and Neurodegeneration Biomarker Status across the Alzheimer Disease Spectrum","volume":"309","author":"CO Lew","year":"2023","journal-title":"Radiology"},{"issue":"1","key":"pone.0330085.ref035","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.jalz.2005.06.003","article-title":"Ways toward an early diagnosis in Alzheimer\u2019s disease: the Alzheimer\u2019s Disease Neuroimaging Initiative (ADNI)","volume":"1","author":"SG Mueller","year":"2005","journal-title":"Alzheimers Dement"},{"key":"pone.0330085.ref036","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1111\/j.1749-6632.2009.05064.x","article-title":"AddNeuroMed--the European collaboration for the discovery of novel biomarkers for Alzheimer\u2019s disease","volume":"1180","author":"S Lovestone","year":"2009","journal-title":"Ann N Y Acad Sci"},{"key":"pone.0330085.ref037","unstructured":"OASIS dataset. https:\/\/sites.wustl.edu\/oasisbrains\/. Accessed 2024 December 10."},{"issue":"9","key":"pone.0330085.ref038","doi-asserted-by":"crossref","first-page":"100804","DOI":"10.1016\/j.patter.2023.100804","article-title":"Leakage and the reproducibility crisis in machine-learning-based science","volume":"4","author":"S Kapoor","year":"2023","journal-title":"Patterns (N Y)"},{"key":"pone.0330085.ref039","first-page":"9355","article-title":"Twins: Revisiting the design of spatial attention in vision transformers","volume":"34","author":"X Chu","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"pone.0330085.ref040","unstructured":"Choromanski K, Likhosherstov V, Dohan D, Song X, Gane A, Sarlos T, et al. Rethinking attention with performers. 2020. doi: 10.48550\/arXiv.2009.14794https:\/\/arxiv.org\/abs\/2009.14794"},{"key":"pone.0330085.ref041","doi-asserted-by":"crossref","unstructured":"Koonce B, Koonce B. MobileNetV3. Convolutional Neural Networks with Swift for Tensorflow: Image Recognition and Dataset Categorization. 2021;125\u201344.","DOI":"10.1007\/978-1-4842-6168-2_11"},{"issue":"1","key":"pone.0330085.ref042","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1186\/s12880-024-01404-3","article-title":"Revolutionizing breast ultrasound diagnostics with EfficientNet-B7 and Explainable AI","volume":"24","author":"M Latha","year":"2024","journal-title":"BMC Med Imaging"},{"issue":"1","key":"pone.0330085.ref043","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1186\/s40537-020-00369-8","article-title":"CatBoost for big data: an interdisciplinary review","volume":"7","author":"JT Hancock","year":"2020","journal-title":"J Big Data"},{"key":"pone.0330085.ref044","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C. Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016. 785\u201394.","DOI":"10.1145\/2939672.2939785"},{"key":"pone.0330085.ref045","doi-asserted-by":"crossref","unstructured":"Berrouachedi A, Jaziri R, Bernard G. Deep extremely randomized trees. In: Neural Information Processing: 26th International Conference, ICONIP 2019, Sydney, NSW, Australia, December 12\u201315, 2019, Proceedings, Part I, 2019. 717\u201329.","DOI":"10.1007\/978-3-030-36708-4_59"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0330085","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T17:32:04Z","timestamp":1755538324000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0330085"}},"subtitle":[],"editor":[{"given":"Mohammad Amin","family":"Fraiwan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2025,8,18]]},"references-count":45,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8,18]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0330085","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,18]]}}}