{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T15:45:36Z","timestamp":1781279136473,"version":"3.54.1"},"reference-count":64,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,6,11]],"date-time":"2024-06-11T00:00:00Z","timestamp":1718064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of Aizu, Japan"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Alzheimer\u2019s Disease (AD) poses a significant health risk globally, particularly among the elderly population. Recent studies underscore its prevalence, with over 50% of elderly Japanese facing a lifetime risk of dementia, primarily attributed to AD. As the most prevalent form of dementia, AD gradually erodes brain cells, leading to severe neurological decline. In this scenario, it is important to develop an automatic AD-detection system, and many researchers have been working to develop an AD-detection system by taking advantage of the advancement of deep learning (DL) techniques, which have shown promising results in various domains, including medical image analysis. However, existing approaches for AD detection often suffer from limited performance due to the complexities associated with training hierarchical convolutional neural networks (CNNs). In this paper, we introduce a novel multi-stage deep neural network architecture based on residual functions to address the limitations of existing AD-detection approaches. Inspired by the success of residual networks (ResNets) in image-classification tasks, our proposed system comprises five stages, each explicitly formulated to enhance feature effectiveness while maintaining model depth. Following feature extraction, a deep learning-based feature-selection module is applied to mitigate overfitting, incorporating batch normalization, dropout and fully connected layers. Subsequently, machine learning (ML)-based classification algorithms, including Support Vector Machines (SVM), Random Forest (RF) and SoftMax, are employed for classification tasks. Comprehensive evaluations conducted on three benchmark datasets, namely ADNI1: Complete 1Yr 1.5T, MIRAID and OASIS Kaggle, demonstrate the efficacy of our proposed model. Impressively, our model achieves accuracy rates of 99.47%, 99.10% and 99.70% for ADNI1: Complete 1Yr 1.5T, MIRAID and OASIS datasets, respectively, outperforming existing systems in binary class problems. Our proposed model represents a significant advancement in the AD-analysis domain.<\/jats:p>","DOI":"10.3390\/jimaging10060141","type":"journal-article","created":{"date-parts":[[2024,6,11]],"date-time":"2024-06-11T03:15:58Z","timestamp":1718075758000},"page":"141","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Residual-Based Multi-Stage Deep Learning Framework for Computer-Aided Alzheimer\u2019s Disease Detection"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6499-1825","authenticated-orcid":false,"given":"Najmul","family":"Hassan","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1238-0464","authenticated-orcid":false,"given":"Abu Saleh","family":"Musa Miah","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7476-2468","authenticated-orcid":false,"given":"Jungpil","family":"Shin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.compbiomed.2015.07.006","article-title":"Probability distribution function-based classification of structural MRI for the detection of Alzheimer\u2019s disease","volume":"64","author":"Beheshti","year":"2015","journal-title":"Comput. Biol. Med."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Rangaswamy, U., Dharshini, S.A.P., Yesudhas, D., and Gromiha, M.M. (2020). VEPAD-Predicting the effect of variants associated with Alzheimer\u2019s disease using machine learning. Comput. Biol. Med., 124.","DOI":"10.1016\/j.compbiomed.2020.103933"},{"key":"ref_3","first-page":"462","article-title":"Alzheimer\u2019s disease research in Japan: A short history, current status and future perspectives toward prevention","volume":"8","author":"Iwatsubo","year":"2021","journal-title":"J. Prev. Alzheimer\u2019s Dis."},{"key":"ref_4","unstructured":"Patterson, C. (2018). World Alzheimer Report 2018, Alzheimer\u2019s Disease International (ADI)."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kong, Z., Zhang, M., Zhu, W., Yi, Y., Wang, T., and Zhang, B. (2022). Multi-modal data Alzheimer\u2019s disease detection based on 3D convolution. Biomed. Signal Process. Control, 75.","DOI":"10.1016\/j.bspc.2022.103565"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Alzheimer\u2019s Association (2018). 2018 Alzheimer\u2019s disease facts and figures. Alzheimer\u2019s Dement., 14, 367\u2013429.","DOI":"10.1016\/j.jalz.2018.02.001"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Alzheimer\u2019s Association (2019). 2019 Alzheimer\u2019s disease facts and figures. Alzheimer\u2019s Dement., 15, 321\u2013387.","DOI":"10.1016\/j.jalz.2019.01.010"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1038\/nrd2896","article-title":"Alzheimer\u2019s disease: Strategies for disease modification","volume":"9","author":"Citron","year":"2010","journal-title":"Nat. Rev. Drug Discov."},{"key":"ref_9","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":"Venugopalan","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_10","first-page":"6773","article-title":"Mild cognitive impairment: A comprehensive review","volume":"10","author":"Mishra","year":"2019","journal-title":"Int. J. Biol. Med. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s40120-017-0069-5","article-title":"Increasing Precision of clinical diagnosis of Alzheimer\u2019s disease using a combined algorithm incorporating clinical and novel biomarker data","volume":"6","author":"Sabbagh","year":"2017","journal-title":"Neurol. Ther."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40708-020-00112-2","article-title":"Application of deep learning in detecting neurological disorders from magnetic resonance images: A survey on the detection of Alzheimer\u2019s disease, Parkinson\u2019s disease and schizophrenia","volume":"7","author":"Noor","year":"2020","journal-title":"Brain Inform."},{"key":"ref_13","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. (TOMM)"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ebrahimighahnavieh, M.A., Luo, S., and Chiong, R. (2020). Deep learning to detect Alzheimer\u2019s disease from neuroimaging: A systematic literature review. Comput. Methods Programs Biomed., 187.","DOI":"10.1016\/j.cmpb.2019.105242"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hassan, N., Miah, A.S.M., and Shin, J. (2024). A Deep Bidirectional LSTM Model Enhanced by Transfer-Learning-Based Feature Extraction for Dynamic Human Activity Recognition. Appl. Sci., 14.","DOI":"10.3390\/app14020603"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1007\/s10278-015-9847-8","article-title":"Methods on skull stripping of MRI head scan images\u2014A review","volume":"29","author":"Kalavathi","year":"2016","journal-title":"J. Digit. Imaging"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhuang, J., Cai, J., Wang, R., Zhang, J., and Zheng, W.S. (2020, January 4\u20138). Deep kNN for medical image classification. Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020: 23rd International Conference, Lima, Peru. Proceedings, Part I 23.","DOI":"10.1007\/978-3-030-59710-8_13"},{"key":"ref_18","first-page":"145","article-title":"Multiclass classification of Alzheimer\u2019s disease using hybrid deep convolutional neural network","volume":"8","author":"Suganthe","year":"2021","journal-title":"Nveo-Nat. Volatiles Essent. Oils J."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1040","DOI":"10.1166\/jmihi.2020.3001","article-title":"Classification of Alzheimer\u2019s disease via eight-layer convolutional neural network with batch normalization and dropout techniques","volume":"10","author":"Jiang","year":"2020","journal-title":"J. Med. Imaging Health Inf."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"101713","DOI":"10.1016\/j.compmedimag.2020.101713","article-title":"A novel CNN based Alzheimer\u2019s disease classification using hybrid enhanced ICA segmented gray matter of MRI","volume":"81","author":"Basheera","year":"2020","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2513107","DOI":"10.1109\/TIM.2021.3107056","article-title":"Alzheimer\u2019s patient analysis using image and gene expression data and explainable-AI to present associated genes","volume":"70","author":"Kamal","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_22","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":"ref_23","doi-asserted-by":"crossref","first-page":"3463","DOI":"10.1038\/s41598-024-53733-6","article-title":"A novel CNN architecture for accurate early detection and classification of Alzheimer\u2019s disease using MRI data","volume":"14","author":"Amer","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"AlSaeed, D., and Omar, S.F. (2022). Brain MRI analysis for Alzheimer\u2019s disease diagnosis using CNN-based feature extraction and machine learning. Sensors, 22.","DOI":"10.3390\/s22082911"},{"key":"ref_25","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January June). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3767","DOI":"10.1007\/s11042-023-15738-7","article-title":"A deep learning framework for early diagnosis of Alzheimer\u2019s disease on MRI images","volume":"83","author":"Arafa","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Loddo, A., Buttau, S., and Di Ruberto, C. (2022). Deep learning based pipelines for Alzheimer\u2019s disease diagnosis: A comparative study and a novel deep-ensemble method. Comput. Biol. Med., 141.","DOI":"10.1016\/j.compbiomed.2021.105032"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"34553","DOI":"10.1109\/ACCESS.2024.3372425","article-title":"Sign Language Recognition Using Graph and General Deep Neural Network Based on Large Scale Dataset","volume":"12","author":"Miah","year":"2024","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"65213","DOI":"10.1109\/ACCESS.2024.3395329","article-title":"Anomaly Detection in Weakly Supervised Videos Using Multistage Graphs and General Deep Learning Based Spatial-Temporal Feature Enhancement","volume":"12","author":"Shin","year":"2024","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"68303","DOI":"10.1109\/ACCESS.2024.3399839","article-title":"Korean Sign Language Alphabet Recognition through the Integration of Handcrafted and Deep Learning-Based Two-Stream Feature Extraction Approach","volume":"12","author":"Shin","year":"2024","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"14487","DOI":"10.1007\/s00521-022-07263-9","article-title":"Two-stage deep learning model for Alzheimer\u2019s disease detection and prediction of the mild cognitive impairment time","volume":"34","author":"Saleh","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"856295","DOI":"10.3389\/fninf.2022.856295","article-title":"Research on Voxel-Based Features Detection and Analysis of Alzheimer\u2019s Disease Using Random Survey Support Vector Machine","volume":"16","author":"Meng","year":"2022","journal-title":"Front. Neuroinformatics"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1109\/OJCS.2024.3370971","article-title":"Hand Gesture Recognition for Multi-Culture Sign Language Using Graph and General Deep Learning Network","volume":"5","author":"Miah","year":"2024","journal-title":"IEEE Open J. Comput. Soc."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Vasant, P., Zelinka, I., and Weber, G.W. (2021). Alzheimer\u2019s Disease Detection Using CNN Based on Effective Dimensionality Reduction Approach. Proceedings of the Intelligent Computing and Optimization: Proceedings of the 3rd International Conference on Intelligent Computing and Optimization 2020 (ICO 2020), Springer.","DOI":"10.1007\/978-3-030-68154-8"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"10415","DOI":"10.1007\/s00521-021-05799-w","article-title":"A CNN based framework for classification of Alzheimer\u2019s disease","volume":"33","author":"AbdulAzeem","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"7751","DOI":"10.1007\/s00500-022-06762-0","article-title":"Diagnosis and classification of Alzheimer\u2019s disease by using a convolution neural network algorithm","volume":"26","year":"2022","journal-title":"Soft Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"11599","DOI":"10.1007\/s00521-021-06149-6","article-title":"A new deep belief network-based multi-task learning for diagnosis of Alzheimer\u2019s disease","volume":"35","author":"Zeng","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"44650","DOI":"10.1109\/ACCESS.2023.3272482","article-title":"DeepCurvMRI: Deep Convolutional Curvelet Transform-based MRI Approach for Early Detection of Alzheimer\u2019s Disease","volume":"11","author":"Chabib","year":"2023","journal-title":"IEEE Access"},{"key":"ref_39","first-page":"199","article-title":"Classification on Alzheimer\u2019s Disease MRI Images with VGG-16 and VGG-19","volume":"Volume 2","author":"Antony","year":"2022","journal-title":"IOT with Smart Systems: Proceedings of ICTIS 2022"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Raza, N., Naseer, A., Tamoor, M., and Zafar, K. (2023). Alzheimer disease classification through transfer learning approach. Diagnostics, 13.","DOI":"10.3390\/diagnostics13040801"},{"key":"ref_41","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":"ref_42","doi-asserted-by":"crossref","first-page":"100305","DOI":"10.1016\/j.imu.2020.100305","article-title":"Automatic classification of cognitively normal, mild cognitive impairment and Alzheimer\u2019s disease using structural MRI analysis","volume":"18","author":"Rallabandi","year":"2020","journal-title":"Inform. Med. Unlocked"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Liu, J., Li, M., Luo, Y., Yang, S., Li, W., and Bi, Y. (2021). Alzheimer\u2019s disease detection using depthwise separable convolutional neural networks. Comput. Methods Programs Biomed., 203.","DOI":"10.1016\/j.cmpb.2021.106032"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2201","DOI":"10.1007\/s13369-021-06131-3","article-title":"Detecting the stages of Alzheimer\u2019s disease with pre-trained deep learning architectures","volume":"47","year":"2022","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Kaplan, E., Dogan, S., Tuncer, T., Baygin, M., and Altunisik, E. (2021). Feed-forward LPQNet based automatic alzheimer\u2019s disease detection model. Comput. Biol. Med., 137.","DOI":"10.1016\/j.compbiomed.2021.104828"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Deepa, N., and Chokkalingam, S. (2022). Optimization of VGG16 utilizing the arithmetic optimization algorithm for early detection of Alzheimer\u2019s disease. Biomed. Signal Process. Control, 74.","DOI":"10.1016\/j.bspc.2021.103455"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hu, Z., Wang, Z., Jin, Y., and Hou, W. (2023). VGG-TSwinformer: Transformer-based deep learning model for early Alzheimer\u2019s disease prediction. Comput. Methods Programs Biomed., 229.","DOI":"10.1016\/j.cmpb.2022.107291"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Carcagn\u00ec, P., Leo, M., Del Coco, M., Distante, C., and De Salve, A. (2023). Convolution neural networks and self-attention learners for Alzheimer dementia diagnosis from brain MRI. Sensors, 23.","DOI":"10.3390\/s23031694"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"170212","DOI":"10.1016\/j.ijleo.2022.170212","article-title":"Automated prediction system for Alzheimer detection based on deep residual autoencoder and Support Vector Machine","volume":"272","author":"Menagadevi","year":"2023","journal-title":"Optik"},{"key":"ref_50","unstructured":"Marcus, D., Buckner, R., Csernansky, J., and Morris, J. (2024, February 01). OASIS-1: Cross-Sectional: Principal Investigators, Morris; P50 AG05681, P01 AG03991, P01 AG026276, R01 AG021910, P20 MH071616, U24 RR021382. Available online: https:\/\/www.kaggle.com\/datasets\/ninadaithal\/imagesoasis\/data."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1162\/jocn.2007.19.9.1498","article-title":"Open Access Series of Imaging Studies (OASIS): Cross-sectional MRI data in young, middle aged, nondemented, and demented older adults","volume":"19","author":"Marcus","year":"2007","journal-title":"J. Cogn. Neurosci."},{"key":"ref_52","unstructured":"(2024, February 01). Minimal Interval Resonance Imaging in Alzheimer\u2019s Disease (MIRIAD). Available online: https:\/\/www.ucl.ac.uk\/drc\/research-clinical-trials\/minimal-interval-resonance-imaging-alzheimers-disease-miriad."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1016\/j.neuroimage.2011.09.015","article-title":"Fsl","volume":"62","author":"Jenkinson","year":"2012","journal-title":"Neuroimage"},{"key":"ref_54","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 7\u20139). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_55","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_56","unstructured":"Bouchard, G. (2007, January 7\u20138). Efficient bounds for the SoftMax function and applications to approximate inference in hybrid models. Proceedings of the NIPS 2007 Workshop for Approximate Bayesian Inference in Continuous\/Hybrid Systems, Whistler, BC, Canada."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., Friedman, J.H., and Friedman, J.H. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"101645","DOI":"10.1016\/j.nicl.2018.101645","article-title":"Automated classification of Alzheimer\u2019s disease and mild cognitive impairment using a single MRI and deep neural networks","volume":"21","author":"Basaia","year":"2019","journal-title":"NeuroImage Clin."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"91969","DOI":"10.1109\/ACCESS.2023.3307702","article-title":"TriAD: A deep ensemble network for Alzheimer classification and localisation","volume":"11","author":"Mercaldo","year":"2023","journal-title":"IEEE Access"},{"key":"ref_60","unstructured":"Paleczny, A., Parab, S., and Zhang, M. (2023). Enhancing Automated and Early Detection of Alzheimer\u2019s Disease Using Out-Of-Distribution Detection. arXiv."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Mohammed, B.A., Senan, E.M., Rassem, T.H., Makbol, N.M., Alanazi, A.A., Al-Mekhlafi, Z.G., Almurayziq, T.S., and Ghaleb, F.A. (2021). Multi-method analysis of medical records and MRI images for early diagnosis of dementia and Alzheimer\u2019s disease based on deep learning and hybrid methods. Electronics, 10.","DOI":"10.3390\/electronics10222860"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Baglat, P., Salehi, A.W., Gupta, A., and Gupta, G. (2020, January 18\u201319). Multiple machine learning models for detection of Alzheimer\u2019s disease using OASIS dataset. Proceedings of the Re-Imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation: IFIP WG 8.6 International Conference on Transfer and Diffusion of IT, TDIT 2020, Tiruchirappalli, India. Proceedings, Part I.","DOI":"10.1007\/978-3-030-64849-7_54"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"96930","DOI":"10.1109\/ACCESS.2022.3204395","article-title":"ADD-Net: An Effective Deep Learning Model for Early Detection of Alzheimer Disease in MRI Scans","volume":"10","author":"Fareed","year":"2022","journal-title":"IEEE Access"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Mggdadi, E., Al-Aiad, A., Al-Ayyad, M.S., and Darabseh, A. (2021, January 24\u201326). Prediction Alzheimer\u2019s disease from MRI images using deep learning. Proceedings of the 2021 12th International Conference on Information and Communication Systems (ICICS), Valencia, Spain.","DOI":"10.1109\/ICICS52457.2021.9464543"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/10\/6\/141\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:56:35Z","timestamp":1760108195000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/10\/6\/141"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,11]]},"references-count":64,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["jimaging10060141"],"URL":"https:\/\/doi.org\/10.3390\/jimaging10060141","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,11]]}}}