{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:03:09Z","timestamp":1774540989592,"version":"3.50.1"},"reference-count":44,"publisher":"Tech Science Press","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2024]]},"DOI":"10.32604\/cmc.2024.046869","type":"journal-article","created":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T03:34:12Z","timestamp":1721360052000},"page":"2081-2098","source":"Crossref","is-referenced-by-count":2,"title":["Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis"],"prefix":"10.32604","volume":"80","author":[{"given":"Atif","family":"Mehmood","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhonglong","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rizwan","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmad Al","family":"Smadi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Farah","family":"Shahid","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shahid","family":"Iqbal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mutasem K.","family":"Alsmadi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yazeed Yasin","family":"Ghadi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Syed Aziz","family":"Shah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mostafa M.","family":"Ibrahim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"17807","published-online":{"date-parts":[[2024]]},"reference":[{"key":"ref1","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\u2019s disease on MRI images","volume":"460","author":"Mehmood","year":"2021","journal-title":"Neuroscience"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1002\/alz.12003","article-title":"Two-stage bayesian gwas of 9576 individuals identifies snp regions that are targeted by mirnas inversely expressed in Alzheimer\u2019s and cancer","volume":"16","author":"Pathak","year":"2020","journal-title":"Alzheimer\u2019s Dementia"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"101625","DOI":"10.1016\/j.media.2019.101625","article-title":"Multi-modal neuroimaging feature selection with consistent metric constraint for diagnosis of Alzheimer\u2019s disease","volume":"60","author":"Hao","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref4","doi-asserted-by":"crossref","first-page":"e3225","DOI":"10.1002\/cnm.3225","article-title":"Diagnosis of Alzheimer\u2019s disease with sobolev gradient-based optimization and 3d convolutional neural network","volume":"35","author":"Goceri","year":"2019","journal-title":"Int. J. Numer. Methods Biomed. Eng."},{"key":"ref5","doi-asserted-by":"crossref","first-page":"85430","DOI":"10.1109\/ACCESS.2019.2924604","article-title":"A very deep densely connected network for compressed sensing MRI","volume":"7","author":"Zeng","year":"2019","journal-title":"IEEE Access"},{"key":"ref6","first-page":"1","article-title":"DRLBTSA: Deep reinforcement learning based task-scheduling algorithm in cloud computing","volume":"83","author":"Sudheer","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref7","first-page":"428","article-title":"Generative adversarial networks with data augmentation and multiple penalty areas for image synthesis","volume":"20","author":"Chen","year":"2023","journal-title":"Int Arab J Inform Technol (IAJIT)"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"343","DOI":"10.4018\/978-1-6684-6697-1.ch019","author":"Dash","year":"2023","journal-title":"Handbook of Research on Quantum Computing for Smart Environments"},{"key":"ref9","first-page":"919","article-title":"Deep learning inpainting model on digital and medical images\u2014A review","volume":"20","author":"Susan","year":"2023","journal-title":"Int. Arab. J. Inform. Technol."},{"key":"ref10","doi-asserted-by":"crossref","first-page":"101694","DOI":"10.1016\/j.media.2020.101694","article-title":"Convolutional neural networks for classification of Alzheimer\u2019s disease: Overview and reproducible evaluation","volume":"63","author":"Wen","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref11","doi-asserted-by":"crossref","first-page":"305","DOI":"10.32604\/csse.2022.018520","article-title":"Early diagnosis of Alzheimer\u2019s disease based on convolutional neural networks","volume":"43","author":"Mehmood","year":"2022","journal-title":"Comput. Syst. Sci. Eng."},{"key":"ref12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-019-1475-2","article-title":"A deep learning approach for automated diagnosis and multi-class classification of alzheimer\u2019s disease stages using resting-state fmri and residual neural networks","volume":"44","author":"Ramzan","year":"2020","journal-title":"J. Med. Syst."},{"key":"ref13","article-title":"A blockchain-based approach for healthcare data interoperability","volume":"15","author":"Hussain","year":"2023","journal-title":"Int. J. Adv. Soft Comput. Appl."},{"key":"ref14","doi-asserted-by":"crossref","first-page":"427","DOI":"10.3390\/brainsci10070427","article-title":"State-of-the-art cnn optimizer for brain tumor segmentation in magnetic resonance images","volume":"10","author":"Yaqub","year":"2020","journal-title":"Brain Sci."},{"key":"ref15","first-page":"1","article-title":"Machine learning techniques for the diagnosis of Alzheimer\u2019s disease: A review","volume":"16","author":"Tanveer","year":"2020","journal-title":"ACM Trans. Multimed. Comput., Commun., Appl."},{"key":"ref16","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1093\/brain\/awaa399","article-title":"Time course of phosphorylated-tau181 in blood across the Alzheimer\u2019s disease spectrum","volume":"144","author":"Moscoso","year":"2021","journal-title":"Brain"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"113730","DOI":"10.1016\/j.bios.2021.113730","article-title":"Acoustofluidic multimodal diagnostic system for Alzheimer\u2019s disease","volume":"196","author":"Hao","year":"2022","journal-title":"Biosens. Bioelectron."},{"key":"ref18","first-page":"172","article-title":"A decision science approach using hybrid EEG feature extraction and GAN-based emotion classification","volume":"27","author":"Khalaf","year":"2023","journal-title":"Adv. Decis. Sci."},{"key":"ref19","doi-asserted-by":"crossref","first-page":"101811","DOI":"10.1016\/j.nicl.2019.101811","article-title":"Machine learning based hierarchical classification of frontotemporal dementia and Alzheimer\u2019s disease","volume":"23","author":"Kim","year":"2019","journal-title":"NeuroImage: Clin."},{"key":"ref20","doi-asserted-by":"crossref","first-page":"84","DOI":"10.3390\/brainsci10020084","article-title":"A deep Siamese convolution neural network for multi-class classification of Alzheimer disease","volume":"10","author":"Mehmood","year":"2020","journal-title":"Brain Sci."},{"key":"ref21","doi-asserted-by":"crossref","first-page":"102917","DOI":"10.1016\/j.nicl.2021.102917","article-title":"Alterations of dynamic redundancy of functional brain subnetworks in Alzheimer\u2019s disease and major depression disorders","volume":"33","author":"Ghanbari","year":"2022","journal-title":"NeuroImage: Clin"},{"key":"ref22","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1038\/s41582-021-00520-w","article-title":"Developing the ATX(N) classification for use across the Alzheimer disease continuum","volume":"17","author":"Hampel","year":"2021","journal-title":"Nat. Rev. Neurol."},{"key":"ref23","doi-asserted-by":"crossref","first-page":"102660","DOI":"10.1016\/j.nicl.2021.102660","article-title":"Differential patterns of gray matter volumes and associated gene expression profiles in cognitively-defined Alzheimer\u2019s disease subgroups","volume":"30","author":"Groot","year":"2021","journal-title":"NeuroImage: Clini"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"101059","DOI":"10.1016\/j.imu.2022.101059","article-title":"SEL-COVIDNET: An intelligent application for the diagnosis of COVID-19 from chest X-rays and CT-scans","volume":"32","author":"Smadi","year":"2022","journal-title":"Inform. Med. Unlocked"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.bspc.2018.02.019","article-title":"Multi-class Alzheimer\u2019s disease classification using image and clinical features","volume":"43","author":"Altaf","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"102199","DOI":"10.1016\/j.nicl.2020.102199","article-title":"Modelling prognostic trajectories of cognitive decline due to Alzheimer\u2019s disease","volume":"26","author":"Giorgio","year":"2020","journal-title":"NeuroImage: Clin"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"73373","DOI":"10.1109\/ACCESS.2019.2920011","article-title":"Ensembles of patch-based classifiers for diagnosis of Alzheimer diseases","volume":"7","author":"Ahmed","year":"2019","journal-title":"IEEE Access"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"333","DOI":"10.3390\/bioengineering10030333","article-title":"Modelling and analysis of hybrid transformation for lossless big medical image compression","volume":"10","author":"Xue","year":"2023","journal-title":"Bioengineering"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"102367","DOI":"10.1016\/j.nicl.2020.102367","article-title":"White matter hyperintensities and neuropsychiatric symptoms in mild cognitive impairment and Alzheimer\u2019s disease","volume":"28","author":"Misquitta","year":"2020","journal-title":"NeuroImage: Clin."},{"key":"ref30","doi-asserted-by":"crossref","first-page":"102226","DOI":"10.1016\/j.nicl.2020.102226","article-title":"Prospective memory in prodromal Alzheimer\u2019s disease: Real world relevance and correlations with cortical thickness and hippocampal subfield volumes","volume":"26","author":"Nurdal","year":"2020","journal-title":"NeuroImage: Clin."},{"key":"ref31","doi-asserted-by":"crossref","first-page":"108701","DOI":"10.1016\/j.jneumeth.2020.108701","article-title":"Deep residual learning for neuroimaging: An application to predict progression to Alzheimer\u2019s disease","volume":"339","author":"Abrol","year":"2020","journal-title":"J. Neurosci. Methods"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1007\/s00521-019-04069-0","article-title":"Refining Parkinson\u2019s neurological disorder identification through deep transfer learning","volume":"32","author":"Naseer","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref33","doi-asserted-by":"crossref","first-page":"109705","DOI":"10.1016\/j.mehy.2020.109705","article-title":"Texture based localization of a brain tumor from MR-images by using a machine learning approach","volume":"141","author":"Rehman","year":"2020","journal-title":"Med. Hypotheses"},{"key":"ref34","first-page":"427","article-title":"Cardiac arrhythmia disease classification using LSTM deep learning approach","volume":"67","author":"Khan","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"ref35","doi-asserted-by":"crossref","first-page":"134971","DOI":"10.1016\/j.neulet.2020.134971","article-title":"Deep learning based mild cognitive impairment diagnosis using structure MR images","volume":"730","author":"Jiang","year":"2020","journal-title":"Neurosci. Lett."},{"key":"ref36","doi-asserted-by":"crossref","first-page":"3798","DOI":"10.1109\/TMECH.2021.3132459","article-title":"Fault detection in gears using fault samples enlarged by a combination of numerical simulation and a generative adversarial network","volume":"27","author":"Gao","year":"2021","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref37","doi-asserted-by":"crossref","first-page":"90319","DOI":"10.1109\/ACCESS.2021.3090474","article-title":"DEMNET: A deep learning model for early diagnosis of Alzheimer diseases and dementia from MR images","volume":"9","author":"Murugan","year":"2021","journal-title":"IEEE Access"},{"key":"ref38","first-page":"1","article-title":"Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: Evaluation in Alzheimer\u2019s disease","volume":"13","author":"Dyrba","year":"2021","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref39","first-page":"329","article-title":"Multiplane convolutional neural network (Mp-CNN) for Alzheimer\u2019s disease classification","volume":"15","author":"Angkoso","year":"2022","journal-title":"Int. J. Intell. Eng. Syst."},{"key":"ref40","doi-asserted-by":"crossref","first-page":"4503","DOI":"10.1117\/1.JMI.8.2.024503","article-title":"Disease neuroimaging initiative, convolutional neural networks for Alzheimer\u2019s disease detection on MRI images","volume":"8","author":"Ebrahimi","year":"2021","journal-title":"J. Med. Imaging"},{"key":"ref41","first-page":"12020","article-title":"Automated classification of Alzheimer\u2019s disease based on MRI image processing using convolutional neural network (CNN) with alexnet architecture","volume":"1844","author":"Fu\u2019Adah","journal-title":"J. Phys.: Conf. Series"},{"key":"ref42","doi-asserted-by":"crossref","first-page":"e100485","DOI":"10.1136\/bmjhci-2021-100485","article-title":"Operationalising fairness in medical AI adoption: Detection of early Alzheimer\u2019s disease with 2D CNN","volume":"29","author":"Heising","year":"2022","journal-title":"BMJ Health Care Inform."},{"key":"ref43","doi-asserted-by":"crossref","first-page":"10","DOI":"10.3389\/fninf.2018.00035","article-title":"Alzheimer\u2019s disease neuroimaging initiative. Classification of Alzheimer\u2019s disease by combination of convolutional and recurrent neural networks using FDG-PET images","volume":"12","author":"Liu","year":"2018","journal-title":"Front. Neuroinform."},{"key":"ref44","doi-asserted-by":"crossref","first-page":"1050777","DOI":"10.3389\/fnins.2022.1050777","article-title":"A transfer learning approach for multiclass classification of Alzheimer\u2019s disease using MRI images","volume":"16","author":"Khan","year":"2023","journal-title":"Front. Neurosci."}],"container-title":["Computers, Materials &amp; Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.techscience.com\/files\/cmc\/2024\/TSP_CMC-80-2\/TSP_CMC_46869\/TSP_CMC_46869.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,6]],"date-time":"2025-03-06T12:11:30Z","timestamp":1741263090000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v80n2\/57582"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":44,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024]]},"published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2024.046869","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]}}}