{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T07:11:36Z","timestamp":1793603496272,"version":"4.5.0"},"reference-count":47,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T00:00:00Z","timestamp":1741132800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["slas-technology.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["SLAS Technology"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1016\/j.slast.2025.100265","type":"journal-article","created":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T06:05:49Z","timestamp":1741241149000},"page":"100265","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":28,"special_numbering":"C","title":["Alzheimer's disease prediction using 3D-CNNs: Intelligent processing of neuroimaging data"],"prefix":"10.1016","volume":"32","author":[{"given":"Atta Ur","family":"Rahman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sania","family":"Ali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bibi","family":"Saqia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zahid","family":"Halim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M.A.","family":"Al-Khasawneh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dina Abdulaziz","family":"AlHammadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Zubair","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Inam","family":"Ullah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meshal","family":"Alharbi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.slast.2025.100265_bib0001","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40708-021-00144-2","article-title":"3D convolutional neural networks-based multiclass classification of Alzheimer's and Parkinson's diseases using PET and SPECT neuroimaging modalities","volume":"8","author":"Tufail","year":"2021","journal-title":"Brain Inform"},{"issue":"24","key":"10.1016\/j.slast.2025.100265_bib0002","doi-asserted-by":"crossref","first-page":"5789","DOI":"10.3390\/molecules25245789","article-title":"Comprehensive review on Alzheimer's disease: causes and treatment","volume":"25","author":"Breijyeh","year":"2020","journal-title":"Molecules"},{"key":"10.1016\/j.slast.2025.100265_bib0003","series-title":"2019 IEEE 17th International Conference on Industrial Informatics (INDIN)","first-page":"1237","article-title":"A survey of disease progression modeling techniques for alzheimer's diseases","volume":"1","author":"Wang","year":"2019"},{"issue":"8","key":"10.1016\/j.slast.2025.100265_bib0004","doi-asserted-by":"crossref","first-page":"1473","DOI":"10.3390\/diagnostics11081473","article-title":"Artificial intelligence for Alzheimer's disease: promise or challenge?","volume":"11","author":"Fabrizio","year":"2021","journal-title":"Diagnostics"},{"issue":"6","key":"10.1016\/j.slast.2025.100265_bib0005","doi-asserted-by":"crossref","first-page":"1567","DOI":"10.14336\/AD.2020.0312","article-title":"The application of artificial intelligence in the genetic study of Alzheimer's disease","volume":"11","author":"Mishra","year":"2020","journal-title":"Aging Dis"},{"issue":"9","key":"10.1016\/j.slast.2025.100265_bib0006","doi-asserted-by":"crossref","first-page":"4184","DOI":"10.3390\/s23094184","article-title":"Revolutionizing the early detection of Alzheimer's disease through non-invasive biomarkers: the role of artificial intelligence and deep learning","volume":"23","author":"Vrahatis","year":"2023","journal-title":"Sensors"},{"issue":"8","key":"10.1016\/j.slast.2025.100265_bib0007","doi-asserted-by":"crossref","first-page":"479","DOI":"10.3390\/brainsci10080479","article-title":"The neuropathological diagnosis of Alzheimer's disease\u2014The challenges of pathological mimics and concomitant pathology","volume":"10","author":"King","year":"2020","journal-title":"Brain Sci"},{"key":"10.1016\/j.slast.2025.100265_bib0008","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2023.3328331","article-title":"Explainable artificial intelligence of multi-level stacking ensemble for detection of Alzheimer's disease based on particle swarm optimization and the sub-scores of cognitive biomarkers","author":"Almohimeed","year":"2023","journal-title":"IEEE Access"},{"issue":"2","key":"10.1016\/j.slast.2025.100265_bib0009","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/TCDS.2022.3189701","article-title":"Multimodal self-paced locality-preserving learning for diagnosis of Alzheimer's disease","volume":"15","author":"Hao","year":"2022","journal-title":"EEE Transac Cognit Developm Syst"},{"issue":"7","key":"10.1016\/j.slast.2025.100265_bib0010","doi-asserted-by":"crossref","first-page":"1531","DOI":"10.3390\/diagnostics12071531","article-title":"An MRI scans-based Alzheimer's disease detection via convolutional neural network and transfer learning","volume":"12","author":"Chui","year":"2022","journal-title":"Diagnostics"},{"issue":"1","key":"10.1016\/j.slast.2025.100265_bib0011","doi-asserted-by":"crossref","first-page":"11258","DOI":"10.1038\/s41598-018-29295-9","article-title":"Structural brain imaging in Alzheimer's disease and mild cognitive impairment: biomarker analysis and shared morphometry database","volume":"8","author":"Ledig","year":"2018","journal-title":"Sci Rep"},{"issue":"2","key":"10.1016\/j.slast.2025.100265_bib0012","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1016\/j.bbe.2021.02.006","article-title":"Application of Artificial Intelligence techniques for the detection of Alzheimer's disease using structural MRI images","volume":"41","author":"Zhao","year":"2021","journal-title":"Biocybernet Biomed Eng"},{"issue":"4","key":"10.1016\/j.slast.2025.100265_bib0013","doi-asserted-by":"crossref","first-page":"602","DOI":"10.3390\/brainsci13040602","article-title":"Automated classification of brain tumors from magnetic resonance imaging using deep learning","volume":"13","author":"Rasheed","year":"2023","journal-title":"Brain Sci"},{"key":"10.1016\/j.slast.2025.100265_bib0014","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1109\/RBME.2018.2796598","article-title":"Quantitative MRI brain studies in mild cognitive impairment and Alzheimer's disease: a methodological review","volume":"11","author":"Leandrou","year":"2018","journal-title":"IEEE Rev Biomed Eng"},{"issue":"7","key":"10.1016\/j.slast.2025.100265_bib0015","doi-asserted-by":"crossref","first-page":"701","DOI":"10.3390\/bioengineering11070701","article-title":"Integrating convolutional neural networks with attention mechanisms for Magnetic resonance imaging-based classification of brain tumors","volume":"11","author":"Rasheed","year":"2024","journal-title":"Bioengineering"},{"issue":"10","key":"10.1016\/j.slast.2025.100265_bib0016","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.3390\/bioengineering10101120","article-title":"Application of deep learning for prediction of alzheimer's disease in PET\/MR imaging","volume":"10","author":"Zhao","year":"2023","journal-title":"Bioengineering"},{"key":"10.1016\/j.slast.2025.100265_bib0017","doi-asserted-by":"crossref","DOI":"10.1016\/j.arr.2024.102497","article-title":"AI-driven innovations in Alzheimer's Disease: integrating early diagnosis, personalized treatment, and prognostic modelling","author":"Kale","year":"2024","journal-title":"Ageing Res. Rev."},{"key":"10.1016\/j.slast.2025.100265_bib0018","doi-asserted-by":"crossref","first-page":"4773","DOI":"10.1109\/TNSRE.2023.3337533","article-title":"Graph reasoning module for Alzheimer's Disease diagnosis: a plug-and-play method","volume":"31","author":"Fan","year":"2023","journal-title":"IEEE Transac Neural Syst Rehabilit Eng"},{"key":"10.1016\/j.slast.2025.100265_bib0019","doi-asserted-by":"crossref","first-page":"29870","DOI":"10.1109\/ACCESS.2021.3059658","article-title":"Volumetric feature-based Alzheimer's disease diagnosis from sMRI data using a convolutional neural network and a deep neural network","volume":"9","author":"Basher","year":"2021","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.slast.2025.100265_bib0020","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.neuroimage.2009.05.056","article-title":"Spatially augmented LPboosting for AD classification with evaluations on the ADNI dataset","volume":"48","author":"Hinrichs","year":"2009","journal-title":"Neuroimage"},{"issue":"3","key":"10.1016\/j.slast.2025.100265_bib0021","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1177\/0891988706291079","article-title":"Alzheimer's disease and the aging brain","volume":"19","author":"Terry","year":"2006","journal-title":"J Geriatr Psychiatry Neurol"},{"issue":"2","key":"10.1016\/j.slast.2025.100265_bib0022","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3492865","article-title":"A comprehensive report on machine learning-based early detection of alzheimer's disease using multi-modal neuroimaging data","volume":"55","author":"Sharma","year":"2022","journal-title":"ACM Comput Surveys (CSUR)"},{"issue":"5","key":"10.1016\/j.slast.2025.100265_bib0023","doi-asserted-by":"crossref","DOI":"10.1016\/j.slast.2024.100193","article-title":"CardioGuard: aI-driven ECG authentication hybrid neural network for predictive health monitoring in telehealth systems","volume":"29","author":"Ahmed","year":"2024","journal-title":"SLAS Technol"},{"key":"10.1016\/j.slast.2025.100265_bib0024","doi-asserted-by":"crossref","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":"10.1016\/j.slast.2025.100265_bib0025","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.neuroimage.2014.10.002","article-title":"Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects","volume":"104","author":"Moradi","year":"2015","journal-title":"Neuroimage"},{"key":"10.1016\/j.slast.2025.100265_bib0026","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2019.105242","article-title":"Deep learning to detect Alzheimer's disease from neuroimaging: a systematic literature review","volume":"187","author":"Ebrahimighahnavieh","year":"2020","journal-title":"Comput Methods Programs Biomed"},{"issue":"22","key":"10.1016\/j.slast.2025.100265_bib0027","doi-asserted-by":"crossref","first-page":"14695","DOI":"10.3390\/su142214695","article-title":"On disharmony in batch normalization and dropout methods for early categorization of Alzheimer's disease","volume":"14","author":"Tufail","year":"2022","journal-title":"Sustainability"},{"issue":"2","key":"10.1016\/j.slast.2025.100265_bib0028","doi-asserted-by":"crossref","first-page":"469","DOI":"10.3390\/electronics12020469","article-title":"A novel framework for classification of different Alzheimer's disease stages using CNN model","volume":"12","author":"Mohi ud din dar","year":"2023","journal-title":"Electronics"},{"issue":"2","key":"10.1016\/j.slast.2025.100265_bib0029","doi-asserted-by":"crossref","DOI":"10.1117\/1.JMI.8.2.024503","article-title":"Convolutional neural networks for Alzheimer's disease detection on MRI images","volume":"8","author":"Ebrahimi","year":"2021","journal-title":"J Med Imag"},{"key":"10.1016\/j.slast.2025.100265_bib0030","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compmedimag.2019.01.005","article-title":"RNN-based longitudinal analysis for diagnosis of Alzheimer's disease","volume":"73","author":"Cui","year":"2019","journal-title":"Computer Med Imag Graph"},{"issue":"12","key":"10.1016\/j.slast.2025.100265_bib0031","article-title":"Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"Vincent","year":"2010","journal-title":"J Mach Learn Res"},{"issue":"5","key":"10.1016\/j.slast.2025.100265_bib0032","doi-asserted-by":"crossref","first-page":"5947","DOI":"10.4249\/scholarpedia.5947","article-title":"Deep belief networks","volume":"4","author":"Hinton","year":"2009","journal-title":"Scholarpedia"},{"issue":"4","key":"10.1016\/j.slast.2025.100265_bib0033","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1007\/s40815-018-0455-x","article-title":"Dual-tree complex wavelet transform-based features for automated alcoholism identification","volume":"20","author":"Sharma","year":"2018","journal-title":"Int J Fuzzy Syst"},{"key":"10.1016\/j.slast.2025.100265_bib0034","series-title":"2017 IEEE International Conference on Imaging systems and techniques (IST)","first-page":"1","article-title":"A deep CNN based multi-class classification of Alzheimer's disease using MRI","author":"Farooq","year":"2017"},{"key":"10.1016\/j.slast.2025.100265_bib0035","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"495","article-title":"Multi scale curriculum CNN for context-aware breast MRI malignancy classification","author":"Haarburger","year":"2019"},{"key":"10.1016\/j.slast.2025.100265_bib0036","doi-asserted-by":"crossref","first-page":"217830","DOI":"10.1109\/ACCESS.2020.3040486","article-title":"3D CNN design for the classification of Alzheimer's disease using brain MRI and PET","volume":"8","author":"Khagi","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.slast.2025.100265_bib0037","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.neuroscience.2021.01.002","article-title":"A transfer learning approach for early diagnosis of Alzheimer's disease on MRI images","volume":"460","author":"Mehmood","year":"2021","journal-title":"Neuroscience"},{"key":"10.1016\/j.slast.2025.100265_bib0038","series-title":"5th International Conference on Computing Methodologies and Communication (ICCMC)","first-page":"1503","article-title":"Alzheimer Disease classification using transfer learning","author":"Acharya","year":"2021"},{"issue":"8","key":"10.1016\/j.slast.2025.100265_bib0039","doi-asserted-by":"crossref","first-page":"8357","DOI":"10.1007\/s12652-020-02568-w","article-title":"Automated categorization of brain tumor from mri using cnn features and svm","volume":"12","author":"Deepak","year":"2021","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"10.1016\/j.slast.2025.100265_bib0040","article-title":"A CAD system for diagnosing Alzheimer's disease using 2D slices and an improved AlexNet-SVM method","volume":"212","author":"Shakarami","year":"2020","journal-title":"Optik (Stuttg)"},{"key":"10.1016\/j.slast.2025.100265_bib0041","series-title":"2020 15th international conference on computer engineering and systems (ICCES)","first-page":"1","article-title":"Alzheimer disease early detection using convolutional neural networks","author":"Ebrahim","year":"2020"},{"issue":"6","key":"10.1016\/j.slast.2025.100265_bib0042","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.3390\/diagnostics11061071","article-title":"Analysis of features of alzheimer's disease: detection of early stage from functional brain changes in magnetic resonance images using a finetuned ResNet18 network","volume":"11","author":"Odusami","year":"2021","journal-title":"Diagnostics"},{"issue":"6","key":"10.1016\/j.slast.2025.100265_bib0043","doi-asserted-by":"crossref","first-page":"5373","DOI":"10.1007\/s13369-020-05193-z","article-title":"Deep learning-based segmentation in classification of Alzheimer's disease","volume":"46","author":"Buvaneswari","year":"2021","journal-title":"Arabian J Sci Eng"},{"key":"10.1016\/j.slast.2025.100265_bib0044","series-title":"International conference on smart homes and health telematics","first-page":"3","article-title":"Alzheimer's disease early detection using a low cost three-dimensional densenet-121 architecture","author":"Solano-Rojas","year":"2020"},{"key":"10.1016\/j.slast.2025.100265_bib0045","first-page":"1337","article-title":"Segmentation of medical images using adaptive region growing","author":"Toennies","year":"2017","journal-title":"Med Imag; Image Process"},{"key":"10.1016\/j.slast.2025.100265_bib0046","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.neuroimage.2007.07.007","article-title":"A fast diffeomorphic image registration algorithm","volume":"38","author":"Ashburner","year":"2007","journal-title":"Neuroimage"},{"key":"10.1016\/j.slast.2025.100265_bib0047","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2020.00259","article-title":"Early detection of Alzheimer's disease using magnetic resonance imaging: a novel approach combining convolutional neural networks and ensemble learning","volume":"14","author":"Pan","year":"2020","journal-title":"Front Neurosci"}],"container-title":["SLAS Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2472630325000238?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2472630325000238?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T01:52:46Z","timestamp":1778896366000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2472630325000238"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":47,"alternative-id":["S2472630325000238"],"URL":"https:\/\/doi.org\/10.1016\/j.slast.2025.100265","relation":{},"ISSN":["2472-6303"],"issn-type":[{"value":"2472-6303","type":"print"}],"subject":[],"published":{"date-parts":[[2025,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Alzheimer's disease prediction using 3D-CNNs: Intelligent processing of neuroimaging data","name":"articletitle","label":"Article Title"},{"value":"SLAS Technology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.slast.2025.100265","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Authors. Published by Elsevier Inc. on behalf of Society for Laboratory Automation and Screening.","name":"copyright","label":"Copyright"}],"article-number":"100265"}}