{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T20:47:45Z","timestamp":1774039665449,"version":"3.50.1"},"reference-count":60,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,4,28]],"date-time":"2021-04-28T00:00:00Z","timestamp":1619568000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2021,4,28]]},"abstract":"<jats:p>The automatic diagnosis of Alzheimer\u2019s disease plays an important role in human health, especially in its early stage. Because it is a neurodegenerative condition, Alzheimer\u2019s disease seems to have a long incubation period. Therefore, it is essential to analyze Alzheimer\u2019s symptoms at different stages. In this paper, the classification is done with several methods of machine learning consisting of <jats:inline-formula>\n                     <a:math xmlns:a=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\">\n                        <a:mi>K<\/a:mi>\n                     <\/a:math>\n                  <\/jats:inline-formula>-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), linear discrimination analysis (LDA), and random forest (RF). Moreover, novel convolutional neural network (CNN) architecture is presented to diagnose Alzheimer\u2019s severity. The relationship between Alzheimer\u2019s patients\u2019 functional magnetic resonance imaging (fMRI) images and their scores on the MMSE is investigated to achieve the aim. The feature extraction is performed based on the robust multitask feature learning algorithm. The severity is also calculated based on the Mini-Mental State Examination score, including low, mild, moderate, and severe categories. Results show that the accuracy of the KNN, SVM, DT, LDA, RF, and presented CNN method is 77.5%, 85.8%, 91.7%, 79.5%, 85.1%, and 96.7%, respectively. Moreover, for the presented CNN architecture, the sensitivity of low, mild, moderate, and severe status of Alzheimer patients is 98.1%, 95.2%,89.0%, and 87.5%, respectively. Based on the findings, the presented CNN architecture classifier outperforms other methods and can diagnose the severity and stages of Alzheimer\u2019s disease with maximum accuracy.<\/jats:p>","DOI":"10.1155\/2021\/5514839","type":"journal-article","created":{"date-parts":[[2021,4,28]],"date-time":"2021-04-28T19:46:40Z","timestamp":1619639200000},"page":"1-15","source":"Crossref","is-referenced-by-count":64,"title":["Diagnosis of Alzheimer\u2019s Disease Severity with fMRI Images Using Robust Multitask Feature Extraction Method and Convolutional Neural Network (CNN)"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8033-9519","authenticated-orcid":true,"given":"Morteza","family":"Amini","sequence":"first","affiliation":[{"name":"Department of Cognitive Modeling, Institute for Cognitive Science Studies, Shahid Beheshti University, Tehran, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0674-4428","authenticated-orcid":true,"given":"Mir Mohsen","family":"Pedram","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran"},{"name":"Department of Cognitive Modeling, Institute for Cognitive Science Studies, Tehran, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5351-6017","authenticated-orcid":true,"given":"AliReza","family":"Moradi","sequence":"additional","affiliation":[{"name":"Department of Clinical Psychology, Faculty of Psychology and Educational Science, Kharazmi University, Tehran, Iran"},{"name":"Department of Cognitive Psychology, Institute for Cognitive Science Studies, Tehran, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5019-6828","authenticated-orcid":true,"given":"Mahshad","family":"Ouchani","sequence":"additional","affiliation":[{"name":"Institute for Cognitive and Brain Science, Shahid Beheshti University, Tehran, Iran"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"crossref","first-page":"101771","DOI":"10.1016\/j.nicl.2019.101771","article-title":"Association of short-term cognitive decline and MCI-to-AD dementia conversion with CSF, MRI, amyloid-and 18F-FDG-PET imaging","volume":"22","author":"J. Ottoy","year":"2019","journal-title":"NeuroImage: Clinical"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1159\/000457118"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.3233\/JAD-142547"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1007\/s10072-011-0636-y"},{"key":"5","first-page":"8","article-title":"Advances and pitfalls in the analysis and interpretation of resting-state FMRI data","volume":"4","author":"D. M. Cole","year":"2010","journal-title":"Frontiers in systems neuroscience"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1016\/j.dadm.2017.03.007"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3009169"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.3390\/electronics8030292"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2016.2636665"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1126\/science.1234330"},{"key":"12","article-title":"Deep learning of fMRI big data: a novel approach to subject-transfer decoding","author":"S. Koyamada","year":"2015"},{"issue":"Part B","key":"13","first-page":"314","article-title":"Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: evaluation using sensorimotor tasks","volume":"145","author":"H. Jang","year":"2017","journal-title":"Neuro Image"},{"key":"14","article-title":"Improving neural networks by preventing co-adaptation of feature detectors","author":"G. E. Hinton","year":"2012"},{"key":"15","first-page":"621","article-title":"Principal sensitivity analysis","author":"S. Koyamada"},{"key":"16","first-page":"1409","article-title":"Generative versus discriminative training of RBMS for classification of fMRI images","author":"T. Schmah"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.07.059"},{"key":"18","first-page":"27","article-title":"Classification on ADHD with deep learning","author":"D. P. Kuang"},{"key":"19","first-page":"68","article-title":"Deep learning for cerebellar ataxia classification and functional score regression","author":"Z. Yang"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2018.10.054"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroscience.2015.08.013"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1177\/1533317520918719"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2015.2429556"},{"key":"24","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"A. Krizhevsky"},{"key":"25","article-title":"Complex brain network analysis and its applications to brain disorders: a survey","volume":"2017","author":"J. Liu","year":"2017","journal-title":"Complexity"},{"issue":"1","key":"26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.26599\/BDMA.2018.9020001","article-title":"Applications of deep learning to MRI images: a survey","volume":"1","author":"J. Liu","year":"2018","journal-title":"Big Data Mining and Analytics"},{"issue":"2","key":"27","first-page":"155","article-title":"Classification of breast cancer using differential evolution and least squares support vector machine","volume":"3","author":"O. S. Soliman","year":"2014","journal-title":"International Journal of Emerging Trends and Technology in Computer Science"},{"key":"28","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1007\/978-981-15-1816-4_4","article-title":"Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer's disease","volume-title":"Deep learning applications","author":"S. Wang","year":"2020"},{"key":"29","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1007\/978-3-030-00919-9_39","article-title":"End-to-end Alzheimer's disease diagnosis and biomarker identification","volume-title":"International Workshop on Machine Learning in Medical Imaging","author":"S. Esmaeilzadeh","year":"2018"},{"key":"30","first-page":"413","article-title":"Alzheimer's disease neuroimaging initiative. Learning longitudinal MRI patterns by SICE and deep learning: assessing the Alzheimer's disease progression","author":"A. Ortiz"},{"key":"31","first-page":"3724","article-title":"DemNet: a convolutional neural network for the detection of Alzheimer's disease and mild cognitive impairment","author":"C. D. Billones"},{"key":"32","first-page":"583","article-title":"Deep learning-based feature representation for AD\/MCI classification","author":"H. I. Suk"},{"key":"33","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2014.06.077"},{"key":"34","first-page":"1015","article-title":"Early diagnosis of Alzheimer's disease with deep learning","author":"S. Liu"},{"key":"35","doi-asserted-by":"publisher","DOI":"10.1007\/s11682-015-9480-7"},{"key":"36","first-page":"816","article-title":"Deep learning-based pipeline to recognize Alzheimer's disease using fMRI data","author":"S. Sarraf"},{"key":"37","article-title":"Alzheimer's disease neuroimaging initiative","volume-title":"DeepAD: Alzheimer's disease classification via deep convolutional neural networks using MRI and fMRI","author":"S. Sarraf","year":"2016"},{"key":"38","first-page":"1","article-title":"Alzheimer's disease classification based on combination of multi-model convolutional networks","author":"F. Li"},{"key":"39","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2017.12.011"},{"key":"40","doi-asserted-by":"publisher","DOI":"10.1007\/s12021-018-9370-4"},{"key":"41","first-page":"1571","article-title":"Visual explanations from deep 3D convolutional neural networks for Alzheimer's disease classification","volume-title":"AMIA Annual Symposium Proceedings","author":"C. Yang","year":"2018"},{"issue":"5","key":"42","first-page":"1","article-title":"Classification of Alzheimer's disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling","volume":"42","author":"S. H. Wang","year":"2018","journal-title":"Journal of medical systems"},{"key":"43","article-title":"3D CNN-based classification using sMRI and MD-DTI images for Alzheimer's disease studies","author":"A. Khvostikov","year":"2018"},{"issue":"1","key":"44","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1109\/JBHI.2017.2655720","article-title":"Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer's disease","volume":"22","author":"J. Shi","year":"2018","journal-title":"IEEE journal of biomedical and health informatics"},{"issue":"2","key":"45","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's disease stages using resting-state fMRI and residual neural networks","volume":"44","author":"F. Ramzan","year":"2020","journal-title":"Journal of medical systems"},{"key":"46","doi-asserted-by":"crossref","DOI":"10.1117\/12.2549038","article-title":"Deep learning of volumetric 3D CNN for fMRI in Alzheimer's disease classification","volume-title":"Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging","author":"H. S. Parmar","year":"2020"},{"key":"47","doi-asserted-by":"publisher","DOI":"10.1007\/s12021-019-09419-w"},{"key":"48","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.01.053"},{"key":"49","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/6690539"},{"key":"50","doi-asserted-by":"crossref","first-page":"589","DOI":"10.4018\/978-1-7998-3441-0.ch030","article-title":"Assessment of graph metrics and lateralization of brain connectivity in progression of Alzheimer's disease using fMRI","volume-title":"Research anthology on diagnosing and treating neurocognitive disorders","author":"B. Bhaskaran","year":"2021"},{"key":"51","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroscience.2020.10.023"},{"key":"52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/00207454.2021.1883602","article-title":"Detection of brain lesion location in MRI images using convolutional neural network and robust PCA","volume":"30","author":"M. Ahmadi","year":"2021","journal-title":"International Journal of Neuroscience"},{"key":"53","first-page":"615","article-title":"Deep residual nets for improved Alzheimer's diagnosis","author":"A. Valliani"},{"key":"54","article-title":"Predicting Alzheimer's disease: a neuroimaging study with 3D convolutional neural networks","author":"A. Payan","year":"2015"},{"key":"55","doi-asserted-by":"crossref","DOI":"10.1155\/2021\/6653879","article-title":"QAIS-DSNN: tumor area segmentation of MRI image with optimized quantum matched-filter technique and deep spiking neural network","volume":"2021","author":"M. Ahmadi","year":"2021","journal-title":"BioMed Research International"},{"key":"56","first-page":"895","article-title":"Robust multitask feature learning","author":"P. Gong"},{"key":"57","doi-asserted-by":"publisher","DOI":"10.1007\/s13748-019-00203-0"},{"key":"58","article-title":"Diagnosis and detection of infected tissue of COVID-19 patients based on lung X-ray image using convolutional neural network approaches","volume":"140, article 110170","author":"S. Hassantabar","year":"2020","journal-title":"Chaos, Solitons & Fractals"},{"issue":"3","key":"59","first-page":"189","article-title":"\"Mini-mental state\": a practical method for grading the cognitive state of patients for the clinician","volume":"12","author":"M. F. Folstein","year":"1975","journal-title":"Journal of psychiatric research"},{"key":"60","first-page":"169","article-title":"Pattern recognition and neural networks","volume-title":"Advanced Course on Artificial Intelligence","author":"S. Theodoridis","year":"1999"}],"container-title":["Computational and Mathematical Methods in Medicine"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2021\/5514839.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2021\/5514839.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2021\/5514839.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,22]],"date-time":"2021-07-22T12:57:35Z","timestamp":1626958655000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/cmmm\/2021\/5514839\/"}},"subtitle":[],"editor":[{"given":"Venkatesan","family":"Rajinikanth","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,4,28]]},"references-count":60,"alternative-id":["5514839","5514839"],"URL":"https:\/\/doi.org\/10.1155\/2021\/5514839","relation":{},"ISSN":["1748-6718","1748-670X"],"issn-type":[{"value":"1748-6718","type":"electronic"},{"value":"1748-670X","type":"print"}],"subject":[],"published":{"date-parts":[[2021,4,28]]}}}