{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T01:50:24Z","timestamp":1780624224612,"version":"3.54.1"},"reference-count":176,"publisher":"Springer Science and Business Media LLC","issue":"26","license":[{"start":{"date-parts":[[2022,8,16]],"date-time":"2022-08-16T00:00:00Z","timestamp":1660608000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,8,16]],"date-time":"2022-08-16T00:00:00Z","timestamp":1660608000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1007\/s11042-022-13506-7","type":"journal-article","created":{"date-parts":[[2022,8,16]],"date-time":"2022-08-16T08:03:50Z","timestamp":1660637030000},"page":"37681-37721","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["The use of artificial neural networks to diagnose Alzheimer\u2019s disease from brain images"],"prefix":"10.1007","volume":"81","author":[{"given":"Saman","family":"Fouladi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali A.","family":"Safaei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noreen Izza","family":"Arshad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. J.","family":"Ebadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0106-7050","authenticated-orcid":false,"given":"Ali","family":"Ahmadian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,8,16]]},"reference":[{"key":"13506_CR1","doi-asserted-by":"publisher","first-page":"108701","DOI":"10.1016\/j.jneumeth.2020.108701","volume":"339","author":"A Abrol","year":"2020","unstructured":"Abrol A, Bhattarai M, Fedorov A, Du Y, Plis S, Calhoun V, Alzheimer\u2019s Disease Neuroimaging Initiative (2020) Deep residual learning for neuroimaging: an application to predict progression to Alzheimer\u2019s disease. J Neurosci Methods 339:108701. https:\/\/doi.org\/10.1016\/j.jneumeth.2020.108701","journal-title":"J Neurosci Methods"},{"issue":"9","key":"13506_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-019-1428-9","volume":"43","author":"UR Acharya","year":"2019","unstructured":"Acharya UR, Fernandes SL, WeiKoh JE, Ciaccio EJ, Fabell MKM, Tanik UJ, Rajinikanth V, Yeong CH (2019) Automated detection of Alzheimer\u2019s disease using brain MRI images\u2013 a study with various feature extraction techniques. J Med Syst 43(9):1\u201314. https:\/\/doi.org\/10.1007\/s10916-019-1428-9","journal-title":"J Med Syst"},{"key":"13506_CR3","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1016\/S0731-7085(99)00272-1","volume":"22","author":"S Agatonovic-Kustrin","year":"2000","unstructured":"Agatonovic-Kustrin S, Beresford R (2000) Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal 22:717\u2013727. https:\/\/doi.org\/10.1016\/S0731-7085(99)00272-1","journal-title":"J Pharm Biomed Anal"},{"key":"13506_CR4","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.pscychresns.2012.11.005","volume":"212","author":"C Aguilar","year":"2013","unstructured":"Aguilar C, Westman E, Muehlboeck J-S, Mecocci P, Vellas B, Tsolaki M, Kloszewska I, Soininen H, Lovestone S, Spenger C, Simmons A, Wahlund LO (2013) Different multivariate techniques for automated classification of MRI data in Alzheimer\u2019s disease and mild cognitive impairment. Psychiatry Res Neuroimaging 212:89\u201398. https:\/\/doi.org\/10.1016\/j.pscychresns.2012.11.005","journal-title":"Psychiatry Res Neuroimaging"},{"key":"13506_CR5","doi-asserted-by":"crossref","unstructured":"Ahmed OB, Fezzani S, Guillevin C et al (2020) DeepMRS: an end-to-end deep neural network for dementia disease detection using MRS data. In: 2020 IEEE 17th international symposium on biomedical imaging (ISBI). Pp 1459\u20131463.","DOI":"10.1109\/ISBI45749.2020.9098419"},{"key":"13506_CR6","doi-asserted-by":"crossref","unstructured":"Akhila DB, Shobhana S, Fred AL, Kumar SN (2016) Robust Alzheimer\u2019s disease classification based on multimodal neuroimaging. In: 2016 IEEE international conference on engineering and technology (ICETECH). Pp 748\u2013752.","DOI":"10.1109\/ICETECH.2016.7569348"},{"issue":"1","key":"13506_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.22034\/jbr.2021.262544.1037","volume":"3","author":"A Altaher","year":"2021","unstructured":"Altaher A, Salekshahrezaee Z et al (2021) Using multi-inception CNN for face emotion recognition. J Bioeng Res 3(1):1\u201312. https:\/\/doi.org\/10.22034\/jbr.2021.262544.1037","journal-title":"J Bioeng Res"},{"key":"13506_CR8","doi-asserted-by":"crossref","unstructured":"Amin-Naji M, Mahdavinataj H, Aghagolzadeh A (2019) Alzheimer\u2019s disease diagnosis from structural MRI using Siamese convolutional neural network. In: 2019 4th international conference on pattern recognition and image analysis (IPRIA). Pp 75\u201379.","DOI":"10.1109\/PRIA.2019.8786031"},{"key":"13506_CR9","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.jneumeth.2017.12.011","volume":"302","author":"N Amoroso","year":"2018","unstructured":"Amoroso N, Diacono D, Fanizzi A, la Rocca M, Monaco A, Lombardi A, Guaragnella C, Bellotti R, Tangaro S, Initiative A\u2019s DN (2018) Deep learning reveals Alzheimer\u2019s disease onset in MCI subjects: results from an international challenge. J Neurosci Methods 302:3\u20139. https:\/\/doi.org\/10.1016\/j.jneumeth.2017.12.011","journal-title":"J Neurosci Methods"},{"key":"13506_CR10","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.radphyschem.2016.08.028","volume":"137","author":"MH Azmi","year":"2017","unstructured":"Azmi MH, Saripan MI, Nordin AJ, Ahmad Saad FF, Abdul Aziz SA, Wan Adnan WA (2017) 18F-FDG PET brain images as features for Alzheimer classification. Radiat Phys Chem 137:135\u2013143. https:\/\/doi.org\/10.1016\/j.radphyschem.2016.08.028","journal-title":"Radiat Phys Chem"},{"key":"13506_CR11","doi-asserted-by":"crossref","unstructured":"B\u00e4ckstr\u00f6m K, Nazari M, Gu IY, Jakola AS (2018) An efficient 3D deep convolutional network for Alzheimer\u2019s disease diagnosis using MR images. In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018). Pp 149\u2013153.","DOI":"10.1109\/ISBI.2018.8363543"},{"key":"13506_CR12","doi-asserted-by":"publisher","first-page":"101645","DOI":"10.1016\/j.nicl.2018.101645","volume":"21","author":"S Basaia","year":"2019","unstructured":"Basaia S, Agosta F, Wagner L, Canu E, Magnani G, Santangelo R, Filippi M, Initiative A's DN (2019) Automated classification of Alzheimer\u2019s disease and mild cognitive impairment using a single MRI and deep neural networks. NeuroImage Clin 21:101645. https:\/\/doi.org\/10.1016\/j.nicl.2018.101645","journal-title":"NeuroImage Clin"},{"key":"13506_CR13","doi-asserted-by":"publisher","first-page":"974","DOI":"10.1016\/j.trci.2019.10.001","volume":"5","author":"S Basheera","year":"2019","unstructured":"Basheera S, Sai Ram MS (2019) Convolution neural network\u2013based Alzheimer\u2019s disease classification using hybrid enhanced independent component analysis based segmented gray matter of T2 weighted magnetic resonance imaging with clinical valuation. Alzheimer\u2019s Dement Transl Res Clin Interv 5:974\u2013986. https:\/\/doi.org\/10.1016\/j.trci.2019.10.001","journal-title":"Alzheimer\u2019s Dement Transl Res Clin Interv"},{"key":"13506_CR14","doi-asserted-by":"publisher","first-page":"12883","DOI":"10.1007\/s11042-018-6287-8","volume":"78","author":"D Baskar","year":"2019","unstructured":"Baskar D, Jayanthi VS, Jayanthi AN (2019) An efficient classification approach for detection of Alzheimer\u2019s disease from biomedical imaging modalities. Multimed Tools Appl 78:12883\u201312915. https:\/\/doi.org\/10.1007\/s11042-018-6287-8","journal-title":"Multimed Tools Appl"},{"key":"13506_CR15","first-page":"205","volume-title":"Shen D","author":"S Basu","year":"2019","unstructured":"Basu S, Wagstyl K, Zandifar A et al (2019) Early prediction of Alzheimer\u2019s disease progression using Variational autoencoders BT - medical image computing and computer assisted intervention \u2013 MICCAI 2019. In: Liu T, Peters TM et al (eds) Shen D. Springer International Publishing, Cham, pp 205\u2013213"},{"key":"13506_CR16","doi-asserted-by":"crossref","unstructured":"Baydargil HB, Park J, Kang D (2019) Classification of Alzheimer\u2019s disease using stacked sparse convolutional autoencoder. In: 2019 19th international conference on control, automation and systems (ICCAS). Pp 891\u2013895.","DOI":"10.23919\/ICCAS47443.2019.8971696"},{"key":"13506_CR17","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.compbiomed.2015.07.006","volume":"64","author":"I Beheshti","year":"2015","unstructured":"Beheshti I, Demirel H (2015) Probability distribution function-based classification of structural MRI for the detection of Alzheimer\u2019s disease. Comput Biol Med 64:208\u2013216. https:\/\/doi.org\/10.1016\/j.compbiomed.2015.07.006","journal-title":"Comput Biol Med"},{"key":"13506_CR18","first-page":"57","volume":"29","author":"F Bert\u00e8","year":"2014","unstructured":"Bert\u00e8 F, Lamponi G, Calabr\u00f2 RS, Bramanti P (2014) Elman neural network for the early identification of cognitive impairment in Alzheimer\u2019s disease. Funct Neurol 29:57\u201365","journal-title":"Funct Neurol"},{"key":"13506_CR19","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1007\/s40846-020-00548-1","volume":"40","author":"Y B-h","year":"2020","unstructured":"B-h Y, J-c C, W-h C et al (2020) Classification of Alzheimer\u2019s disease from 18F-FDG and 11C-PiB PET imaging biomarkers using support vector machine. J Med Biol Eng 40:545\u2013554","journal-title":"J Med Biol Eng"},{"key":"13506_CR20","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1503\/jpn.180016","volume":"44","author":"N Bhagwat","year":"2019","unstructured":"Bhagwat N, Pipitone J, Voineskos AN, Chakravarty MM, Initiative A\u2019s DN (2019) An artificial neural network model for clinical score prediction in Alzheimer disease using structural neuroimaging measures. J Psychiatry Neurosci 44:246\u2013260. https:\/\/doi.org\/10.1503\/jpn.180016","journal-title":"J Psychiatry Neurosci"},{"key":"13506_CR21","doi-asserted-by":"crossref","unstructured":"Bhatkoti P, Paul M (2016) Early diagnosis of Alzheimer\u2019s disease: a multi-class deep learning framework with modified k-sparse autoencoder classification. In: 2016 international conference on image and vision computing New Zealand (IVCNZ). Pp 1\u20135.","DOI":"10.1109\/IVCNZ.2016.7804459"},{"key":"13506_CR22","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1016\/j.neucom.2018.11.111","volume":"392","author":"X Bi","year":"2020","unstructured":"Bi X, Li S, Xiao B, Li Y, Wang G, Ma X (2020) Computer aided Alzheimer\u2019s disease diagnosis by an unsupervised deep learning technology. Neurocomputing 392:296\u2013304. https:\/\/doi.org\/10.1016\/j.neucom.2018.11.111","journal-title":"Neurocomputing"},{"issue":"1","key":"13506_CR23","doi-asserted-by":"publisher","first-page":"76","DOI":"10.17925\/ENR.2009.04.01.76","volume":"4","author":"H Bidmon","year":"2009","unstructured":"Bidmon H, Speckmann E-J, Zilles K (2009) Epilepsy seizure semiology, neurotransmitter receptors and cellular-stress responses in Pentylenetetrazole models of epilepsy. Eur Neurol Rev 4(1):76\u201380","journal-title":"Eur Neurol Rev"},{"issue":"5","key":"13506_CR24","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1007\/s10278-019-00265-5","volume":"33","author":"TA Bin","year":"2020","unstructured":"Bin TA, Ma Y-K, Zhang Q-N (2020) Binary classification of Alzheimer\u2019s disease using sMRI imaging modality and deep learning. J Digit Imaging 33(5):1073\u20131090. https:\/\/doi.org\/10.1007\/s10278-019-00265-5","journal-title":"J Digit Imaging"},{"key":"13506_CR25","doi-asserted-by":"publisher","first-page":"58213","DOI":"10.1109\/ACCESS.2018.2871977","volume":"6","author":"A Chaddad","year":"2018","unstructured":"Chaddad A, Desrosiers C, Niazi T (2018) Deep Radiomic analysis of MRI related to Alzheimer\u2019s disease. IEEE Access 6:58213\u201358221. https:\/\/doi.org\/10.1109\/ACCESS.2018.2871977","journal-title":"IEEE Access"},{"key":"13506_CR26","first-page":"303","volume-title":"Ren J","author":"Y Chen","year":"2018","unstructured":"Chen Y, Jia H, Huang Z, Xia Y (2018) Early identification of Alzheimer\u2019s disease using an ensemble of 3D convolutional neural networks and magnetic resonance imaging BT - advances in brain inspired cognitive systems. In: Hussain A, Zheng J et al (eds) Ren J. Springer International Publishing, Cham, pp 303\u2013311"},{"key":"13506_CR27","first-page":"106","volume-title":"Wang Q","author":"D Cheng","year":"2017","unstructured":"Cheng D, Liu M (2017) Classification of Alzheimer\u2019s disease by cascaded convolutional neural networks using PET images BT - machine learning in medical imaging. In: Shi Y, Suk H-I, Suzuki K (eds) Wang Q. Springer International Publishing, Cham, pp 106\u2013113"},{"key":"13506_CR28","doi-asserted-by":"crossref","unstructured":"Cheng D, Liu M (2017) CNNs based multi-modality classification for AD diagnosis. In: 2017 10th international congress on image and signal processing, BioMedical engineering and informatics (CISP-BMEI). Pp 1\u20135.","DOI":"10.1109\/CISP-BMEI.2017.8302281"},{"key":"13506_CR29","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1016\/j.neuroimage.2011.05.083","volume":"58","author":"A Chincarini","year":"2011","unstructured":"Chincarini A, Bosco P, Calvini P, Gemme G, Esposito M, Olivieri C, Rei L, Squarcia S, Rodriguez G, Bellotti R, Cerello P, de Mitri I, Retico A, Nobili F, Initiative A\u2019s DN (2011) Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer\u2019s disease. Neuroimage 58:469\u2013480. https:\/\/doi.org\/10.1016\/j.neuroimage.2011.05.083","journal-title":"Neuroimage"},{"key":"13506_CR30","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.bbr.2018.02.017","volume":"344","author":"H Choi","year":"2018","unstructured":"Choi H, Jin KH (2018) Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging. Behav Brain Res 344:103\u2013109. https:\/\/doi.org\/10.1016\/j.bbr.2018.02.017","journal-title":"Behav Brain Res"},{"key":"13506_CR31","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1007\/s00259-019-04538-7","volume":"47","author":"H Choi","year":"2020","unstructured":"Choi H, Kim YK, Yoon EJ et al (2020) Cognitive signature of brain FDG PET based on deep learning: domain transfer from Alzheimer\u2019s disease to Parkinson\u2019s disease. Eur J Nucl Med Mol Imaging 47:403\u2013412. https:\/\/doi.org\/10.1007\/s00259-019-04538-7","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"13506_CR32","doi-asserted-by":"publisher","unstructured":"Chrysos G, Moschoglou S, Bouritsas G et al (2021) Deep polynomial neural networks. IEEE Trans Patt Mach Intell. https:\/\/doi.org\/10.1109\/TPAMI.2021.3058891,1","DOI":"10.1109\/TPAMI.2021.3058891,1"},{"key":"13506_CR33","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.neucom.2013.01.065","volume":"128","author":"D Chyzhyk","year":"2014","unstructured":"Chyzhyk D, Savio A, Gra\u00f1a M (2014) Evolutionary ELM wrapper feature selection for Alzheimer\u2019s disease CAD on anatomical brain MRI. Neurocomputing 128:73\u201380. https:\/\/doi.org\/10.1016\/j.neucom.2013.01.065","journal-title":"Neurocomputing"},{"key":"13506_CR34","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273\u2013297. https:\/\/doi.org\/10.1007\/BF00994018","journal-title":"Mach Learn"},{"key":"13506_CR35","doi-asserted-by":"crossref","unstructured":"Dehghan H, Pouyan AA, Hassanpour H (2011) SVM-based diagnosis of the Alzheimer\u2019s disease using 18F-FDG PET with fisher discriminant rate. In: 2011 18th Iranian conference of biomedical engineering (ICBME). Pp 37\u201342","DOI":"10.1109\/ICBME.2011.6168581"},{"key":"13506_CR36","doi-asserted-by":"publisher","first-page":"962","DOI":"10.4103\/1673-5374.233433","volume":"13","author":"SI Dimitriadis","year":"2018","unstructured":"Dimitriadis SI, Liparas D, Initiative ADN (2018) How random is the random forest? Random forest algorithm on the service of structural imaging biomarkers for Alzheimer\u2019s disease: from Alzheimer\u2019s disease neuroimaging initiative (ADNI) database. Neural Regen Res 13:962\u2013970. https:\/\/doi.org\/10.4103\/1673-5374.233433","journal-title":"Neural Regen Res"},{"key":"13506_CR37","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/s12021-019-09419-w","volume":"18","author":"NT Duc","year":"2020","unstructured":"Duc NT, Ryu S, Qureshi MNI, Choi M, Lee KH, Lee B (2020) 3D-deep learning based automatic diagnosis of Alzheimer\u2019s disease with joint MMSE prediction using resting-state fMRI. Neuroinformatics 18:71\u201386. https:\/\/doi.org\/10.1007\/s12021-019-09419-w","journal-title":"Neuroinformatics"},{"key":"13506_CR38","series-title":"Advances in intelligent systems and computing, vol","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1007\/978-3-030-66501-2_15","volume-title":"Allahviranloo T. Progress in intelligent decision science. IDS 2020","author":"MJ Ebadi","year":"2021","unstructured":"Ebadi MJ, Jafari H (2021) Solving a class of optimal control problems by using Chebyshev polynomials and recurrent neural networks. In: Salahshour S, Arica N (eds) Allahviranloo T. Progress in intelligent decision science. IDS 2020, Advances in intelligent systems and computing, vol, vol 1301. Springer, Cham, pp 185\u2013194. https:\/\/doi.org\/10.1007\/978-3-030-66501-2_15"},{"key":"13506_CR39","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.neucom.2017.01.010","volume":"235","author":"MJ Ebadi","year":"2017","unstructured":"Ebadi MJ, Hosseini A, Hosseini MM (2017) A projection type steepest descent neural network for solving a class of nonsmooth optimization problems. Neurocomputing 235:164\u2013181. https:\/\/doi.org\/10.1016\/j.neucom.2017.01.010","journal-title":"Neurocomputing"},{"key":"13506_CR40","unstructured":"Ebadi MJ, Hosseini MM, Karbassi SM (2018) An efficient one-layer recurrent neural network for solving a class of nonsmooth pseudoconvex optimization problems. J Theor Appl Inf Technol 96(7):1999\u20132014. Retrieved from http:\/\/www.jatit.org\/volumes\/Vol96No7\/21Vol96No7.pdf"},{"key":"13506_CR41","unstructured":"Ebadi MJ, Hosseini A, Jafari H (2020) An efficient one-layer recurrent neural network for solving a class of nonsmooth optimization problems. J New Res Math 6 (24):97\u2013110. Retrived from http:\/\/journals.srbiau.ac.ir\/article_15615_f34599f523793828ae53dca49834f495.pdf"},{"key":"13506_CR42","doi-asserted-by":"crossref","unstructured":"Ebrahimi-Ghahnavieh A, Luo S, Chiong R (2019) Transfer learning for Alzheimer\u2019s disease detection on MRI images. In: 2019 IEEE international conference on industry 4.0, artificial intelligence, and communications technology (IAICT). Pp 133\u2013138.","DOI":"10.1109\/ICIAICT.2019.8784845"},{"key":"13506_CR43","first-page":"3","volume-title":"Suzuki K","author":"F Eitel","year":"2019","unstructured":"Eitel F, Ritter K (2019) Testing the robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer\u2019s disease classification BT - interpretability of machine intelligence in medical image computing and multimodal learning for clinical decision support. In: Reyes M, Syeda-Mahmood T et al (eds) Suzuki K. Springer International Publishing, Cham, pp 3\u201311"},{"key":"13506_CR44","doi-asserted-by":"crossref","unstructured":"El-Gamal FEA, Elmogy MM, Ghazal M et al (2017) A novel CAD system for local and global early diagnosis of Alzheimer\u2019s disease based on PIB-PET scans. In: 2017 IEEE international conference on image processing (ICIP). Pp 3270\u20133274.","DOI":"10.1109\/ICIP.2017.8296887"},{"key":"13506_CR45","first-page":"337","volume-title":"Shi Y","author":"S Esmaeilzadeh","year":"2018","unstructured":"Esmaeilzadeh S, Belivanis DI, Pohl KM, Adeli E (2018) End-to-end Alzheimer\u2019s disease diagnosis and biomarker identification BT - machine learning in medical imaging. In: Suk H-I, Liu M (eds) Shi Y. Springer International Publishing, Cham, pp 337\u2013345"},{"key":"13506_CR46","doi-asserted-by":"publisher","first-page":"383","DOI":"10.14736\/kyb-2020-3-0383","volume":"56","author":"S Ezazipour","year":"2020","unstructured":"Ezazipour S, Golbabai A (2020) A globally convergent neurodynamics optimization model for mathematical programming with equilibrium constraints. Kybernetika 56:383\u2013409. https:\/\/doi.org\/10.14736\/kyb-2020-3-0383","journal-title":"Kybernetika"},{"key":"13506_CR47","doi-asserted-by":"crossref","unstructured":"Farooq A, Anwar S, Awais M, Rehman S (2017) A deep CNN based multi-class classification of Alzheimer\u2019s disease using MRI. In: 2017 IEEE international conference on imaging systems and techniques (IST). Pp 1\u20136","DOI":"10.1109\/IST.2017.8261460"},{"key":"13506_CR48","doi-asserted-by":"crossref","unstructured":"Farooq A, Anwar S, Awais M, Alnowami M (2017) Artificial intelligence based smart diagnosis of alzheimer\u2019s disease and mild cognitive impairment. In: 2017 international smart cities conference (ISC2). Pp 1\u20134.","DOI":"10.1109\/ISC2.2017.8090871"},{"key":"13506_CR49","first-page":"138","volume-title":"Rekik I","author":"C Feng","year":"2018","unstructured":"Feng C, Elazab A, Yang P et al (2018) 3D convolutional neural network and stacked bidirectional recurrent neural network for Alzheimer\u2019s disease diagnosis BT - PRedictive intelligence in MEdicine. In: Unal G, Adeli E, Park SH (eds) Rekik I. Springer International Publishing, Cham, pp 138\u2013146"},{"key":"13506_CR50","doi-asserted-by":"publisher","first-page":"63605","DOI":"10.1109\/ACCESS.2019.2913847","volume":"7","author":"C Feng","year":"2019","unstructured":"Feng C, Elazab A, Yang P, Wang T, Zhou F, Hu H, Xiao X, Lei B (2019) Deep learning framework for Alzheimer\u2019s disease diagnosis via 3D-CNN and FSBi-LSTM. IEEE Access 7:63605\u201363618. https:\/\/doi.org\/10.1109\/ACCESS.2019.2913847","journal-title":"IEEE Access"},{"key":"13506_CR51","doi-asserted-by":"crossref","unstructured":"Forouzannezhad P, Abbaspour A, Li C et al (2018) A deep neural network approach for early diagnosis of mild cognitive impairment using multiple features. In: 2018 17th IEEE international conference on machine learning and applications (ICMLA). Pp 1341\u20131346.","DOI":"10.1109\/ICMLA.2018.00218"},{"key":"13506_CR52","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1016\/j.comcom.2021.06.011","volume":"176","author":"S Fouladi","year":"2021","unstructured":"Fouladi S, Ebadi MJ, Safaei AA, Bajuri MY, Ahmadian A (2021) Efficient deep neural networks for classification of COVID-19 based on CT images: virtualization via software defined radio. Comput Commun 176:234\u2013248. https:\/\/doi.org\/10.1016\/j.comcom.2021.06.011","journal-title":"Comput Commun"},{"key":"13506_CR53","doi-asserted-by":"publisher","first-page":"102290","DOI":"10.1016\/j.nicl.2020.102290","volume":"27","author":"F Gao","year":"2020","unstructured":"Gao F, Yoon H, Xu Y, Goradia D, Luo J, Wu T, Su Y, Initiative A's DN (2020) AD-NET: age-adjust neural network for improved MCI to AD conversion prediction. NeuroImage Clin 27:102290. https:\/\/doi.org\/10.1016\/j.nicl.2020.102290","journal-title":"NeuroImage Clin"},{"key":"13506_CR54","doi-asserted-by":"crossref","unstructured":"Gao XW, Hui R (2016) A deep learning based approach to classification of CT brain images. In: 2016 SAI computing conference (SAI). Pp 28\u201331","DOI":"10.1109\/SAI.2016.7555958"},{"key":"13506_CR55","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.cmpb.2016.10.007","volume":"138","author":"XW Gao","year":"2017","unstructured":"Gao XW, Hui R, Tian Z (2017) Classification of CT brain images based on deep learning networks. Comput Methods Prog Biomed 138:49\u201356. https:\/\/doi.org\/10.1016\/j.cmpb.2016.10.007","journal-title":"Comput Methods Prog Biomed"},{"key":"13506_CR56","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.bspc.2016.01.009","volume":"27","author":"I Garali","year":"2016","unstructured":"Garali I, Adel M, Bourennane S, Guedj E (2016) Brain region ranking for 18FDG-PET computer-aided diagnosis of Alzheimer\u2019s disease. Biomed Signal Process Control 27:15\u201323. https:\/\/doi.org\/10.1016\/j.bspc.2016.01.009","journal-title":"Biomed Signal Process Control"},{"key":"13506_CR57","first-page":"957","volume-title":"Springer","author":"M Garc\u00eda-Sebasti\u00e1n","year":"2009","unstructured":"Garc\u00eda-Sebasti\u00e1n M, Savio A, Gra\u00f1a M, Villan\u00faa J (2009) On the use of morphometry based features for Alzheimer\u2019s disease detection on MRI BT - bio-inspired systems: computational and ambient intelligence. In: Cabestany J, Sandoval F, Prieto A, Corchado JM (eds) Springer. Berlin Heidelberg, Berlin, Heidelberg, pp 957\u2013964"},{"key":"13506_CR58","doi-asserted-by":"publisher","first-page":"103811","DOI":"10.1016\/j.rinp.2020.103811","volume":"21","author":"N Ghorui","year":"2021","unstructured":"Ghorui N, Ghosh A, Mondal SP, Bajuri MY, Ahmadian A, Salahshour S, Ferrara M (2021) Identification of dominant risk factor involved in spread of COVID-19 using hesitant fuzzy MCDM methodology. Results Phys 21:103811. https:\/\/doi.org\/10.1016\/j.rinp.2020.103811","journal-title":"Results Phys"},{"key":"13506_CR59","doi-asserted-by":"publisher","first-page":"3887","DOI":"10.1007\/s00521-019-04391-7","volume":"32","author":"A Golbabai","year":"2020","unstructured":"Golbabai A, Ezazipour S (2020) A projection-based recurrent neural network and its application in solving convex quadratic bilevel optimization problems. Neural Comput Appl 32:3887\u20133900. https:\/\/doi.org\/10.1007\/s00521-019-04391-7","journal-title":"Neural Comput Appl"},{"key":"13506_CR60","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/j.eswa.2017.04.016","volume":"82","author":"A Golbabai","year":"2017","unstructured":"Golbabai A, Ezazipour SA (2017) High-performance nonlinear dynamic scheme for the solution of equilibrium constrained optimization problems. Expert Syst Appl 82:291\u2013300. https:\/\/doi.org\/10.1016\/j.eswa.2017.04.016","journal-title":"Expert Syst Appl"},{"key":"13506_CR61","first-page":"2661","volume":"1406","author":"IJ Goodfellow","year":"2014","unstructured":"Goodfellow IJ, Pouget-Abadie J, Mirza M et al (2014) Generative adversarial. Networks, arXiv 1406:2661","journal-title":"Networks, arXiv"},{"key":"13506_CR62","volume-title":"Deep learning","author":"IJ Goodfellow","year":"2016","unstructured":"Goodfellow IJ, Bengio Y, Courville A (2016) Deep learning. MIT Press"},{"key":"13506_CR63","doi-asserted-by":"crossref","unstructured":"Gunawardena KANNP, Rajapakse RN, Kodikara ND (2017) Applying convolutional neural networks for pre-detection of alzheimer\u2019s disease from structural MRI data. In: 2017 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP). pp 1\u20137","DOI":"10.1109\/M2VIP.2017.8211486"},{"key":"13506_CR64","doi-asserted-by":"crossref","unstructured":"Guo J, Qiu W, Li X et al (2019) Predicting Alzheimer\u2019s disease by hierarchical graph convolution from positron emission tomography imaging. In: 2019 IEEE international conference on big data (big data). Pp 5359\u20135363.","DOI":"10.1109\/BigData47090.2019.9005971"},{"key":"13506_CR65","first-page":"658","volume-title":"Cheng X","author":"K Han","year":"2019","unstructured":"Han K, Pan H, Gao R et al (2019) Multimodal 3D convolutional neural networks for classification of brain disease using structural MR and FDG-PET images BT - data science. In: Jing W, Song X, Lu Z (eds) Cheng X. Springer Singapore, Singapore, pp 658\u2013668"},{"key":"13506_CR66","doi-asserted-by":"crossref","unstructured":"He G, Ping A, Wang X, Zhu Y (2019) Alzheimer\u2019s disease diagnosis model based on three-dimensional full convolutional DenseNet. In: 2019 10th international conference on information Technology in Medicine and Education (ITME). Pp 13\u201317.","DOI":"10.1109\/ITME.2019.00014"},{"key":"13506_CR67","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). Pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"13506_CR68","doi-asserted-by":"crossref","unstructured":"Herrera LJ, Rojas I, Pomares H et al (2013) Classification of MRI images for Alzheimer\u2019s disease detection. In: 2013 international conference on social computing. Pp 846\u2013851","DOI":"10.1109\/SocialCom.2013.127"},{"key":"13506_CR69","unstructured":"Heydarpoor F, Karbassi SM, Bidabadi N, Ebadi MJ (2020) Solving multi-objective functions for cancer treatment by using metaheuristic algorithms. Int J Comb Optim Probl Informatics 11(3):61\u201375 Retrieved from https:\/\/www.ijcopi.org\/ojs\/article\/view\/124"},{"key":"13506_CR70","doi-asserted-by":"publisher","first-page":"18","DOI":"10.9781\/ijimai.2020.11.011","volume":"6","author":"F Heydarpour","year":"2020","unstructured":"Heydarpour F, Abbasi E, Ebadi MJ, Karbassi SM (2020) Solving an optimal control problem of cancer treatment by artificial neural networks. Int J Interact Multimed Artif Intell 6:18\u201325. https:\/\/doi.org\/10.9781\/ijimai.2020.11.011","journal-title":"Int J Interact Multimed Artif Intell"},{"key":"13506_CR71","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.media.2017.01.008","volume":"37","author":"S H-i","year":"2017","unstructured":"H-i S, S-w L, Shen D (2017) Deep ensemble learning of sparse regression models for brain disease diagnosis. Med Image Anal 37:101\u2013113","journal-title":"Med Image Anal"},{"key":"13506_CR72","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"13506_CR73","doi-asserted-by":"crossref","unstructured":"Hon M, Khan NM (2017) Towards Alzheimer\u2019s disease classification through transfer learning. In: 2017 IEEE international conference on bioinformatics and biomedicine (BIBM). Pp 1166\u20131169.","DOI":"10.1109\/BIBM.2017.8217822"},{"key":"13506_CR74","doi-asserted-by":"publisher","first-page":"80893","DOI":"10.1109\/ACCESS.2019.2919385","volume":"7","author":"X Hong","year":"2019","unstructured":"Hong X, Lin R, Yang C, Zeng N, Cai C, Gou J, Yang J (2019) Predicting Alzheimer\u2019s disease using LSTM. IEEE Access 7:80893\u201380901. https:\/\/doi.org\/10.1109\/ACCESS.2019.2919385","journal-title":"IEEE Access"},{"key":"13506_CR75","doi-asserted-by":"crossref","unstructured":"Hosseini-Asl E, Keynton R, El-Baz A (2016) Alzheimer\u2019s disease diagnostics by adaptation of 3D convolutional network. In: 2016 IEEE international conference on image processing (ICIP). Pp 126\u2013130","DOI":"10.1109\/ICIP.2016.7532332"},{"key":"13506_CR76","doi-asserted-by":"crossref","unstructured":"Hu C, Ju R, Shen Y et al (2016) Clinical decision support for Alzheimer\u2019s disease based on deep learning and brain network. In: 2016 IEEE international conference on communications (ICC). Pp 1\u20136.","DOI":"10.1109\/ICC.2016.7510831"},{"key":"13506_CR77","first-page":"213","volume-title":"Zeng Y","author":"J Islam","year":"2017","unstructured":"Islam J, Zhang Y (2017) A novel deep learning based multi-class classification method for Alzheimer\u2019s disease detection using brain MRI data BT - brain informatics. In: He Y, Kotaleski JH et al (eds) Zeng Y. Springer International Publishing, Cham, pp 213\u2013222"},{"key":"13506_CR78","first-page":"359","volume-title":"Wang S","author":"J Islam","year":"2018","unstructured":"Islam J, Zhang Y (2018) Deep convolutional neural networks for automated diagnosis of Alzheimer\u2019s disease and mild cognitive impairment using 3D brain MRI BT - brain informatics. In: Yamamoto V, Su J et al (eds) Wang S. Springer International Publishing, Cham, pp 359\u2013369"},{"issue":"2","key":"13506_CR79","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40708-018-0080-3","volume":"5","author":"J Islam","year":"2018","unstructured":"Islam J, Zhang Y (2018) Brain MRI analysis for Alzheimer\u2019s disease diagnosis using an ensemble system of deep convolutional neural networks. Brain Informatics 5(2):1\u20134. https:\/\/doi.org\/10.1186\/s40708-018-0080-3","journal-title":"Brain Informatics"},{"key":"13506_CR80","doi-asserted-by":"crossref","unstructured":"Jabason E, Ahmad MO, Swamy MNS (2018) Shearlet based stacked convolutional network for multiclass diagnosis of Alzheimer\u2019s disease using the Florbetapir PET amyloid imaging data. In: 2018 16th IEEE international new circuits and systems conference (NEWCAS). Pp 344\u2013347.","DOI":"10.1109\/NEWCAS.2018.8585550"},{"key":"13506_CR81","doi-asserted-by":"publisher","first-page":"103813","DOI":"10.1016\/j.rinp.2021.103813","volume":"21","author":"N Jain","year":"2021","unstructured":"Jain N, Jhunthra S, Garg H, Gupta V, Mohan S, Ahmadian A, Salahshour S, Ferrara M (2021) Prediction modelling of COVID using machine learning methods from B-cell dataset. Results Phys 21:103813. https:\/\/doi.org\/10.1016\/j.rinp.2021.103813","journal-title":"Results Phys"},{"key":"13506_CR82","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.cogsys.2018.12.015","volume":"57","author":"R Jain","year":"2019","unstructured":"Jain R, Jain N, Aggarwal A, Hemanth DJ (2019) Convolutional neural network based Alzheimer\u2019s disease classification from magnetic resonance brain images. Cogn Syst Res 57:147\u2013159. https:\/\/doi.org\/10.1016\/j.cogsys.2018.12.015","journal-title":"Cogn Syst Res"},{"key":"13506_CR83","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1007\/s11063-020-10369-7","volume":"53","author":"N Jamali","year":"2021","unstructured":"Jamali N, Sadegheih A, Lotfi MM, Wood LC, Ebadi MJ (2021) Estimating the depth of anesthesia during the induction by a novel adaptive neuro-fuzzy inference system: a case study. Neural Process Lett 53:131\u2013175. https:\/\/doi.org\/10.1007\/s11063-020-10369-7","journal-title":"Neural Process Lett"},{"issue":"4","key":"13506_CR84","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1016\/j.irbm.2020.06.006","volume":"42","author":"RR Janghel","year":"2021","unstructured":"Janghel RR, Rathore YK (2021) Deep convolution neural network based system for early diagnosis of Alzheimer\u2019s disease. IRBM 42(4):258\u2013267. https:\/\/doi.org\/10.1016\/j.irbm.2020.06.006","journal-title":"IRBM"},{"key":"13506_CR85","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.patrec.2020.03.014","volume":"135","author":"K Jew","year":"2020","unstructured":"Jew K, Jahmunah V, T-h P et al (2020) Automated detection of Alzheimer\u2019s disease using bi-directional empirical model decomposition. Patt Recognit Lett [Internet] 135:106\u2013113","journal-title":"Patt Recognit Lett [Internet]"},{"key":"13506_CR86","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1109\/TCBB.2017.2776910","volume":"16","author":"R Ju","year":"2019","unstructured":"Ju R, Hu C, Zhou P, Li Q (2019) Early diagnosis of Alzheimer\u2019s disease based on resting-state brain networks and deep learning. IEEE\/ACM Trans Comput Biol Bioinforma 16:244\u2013257. https:\/\/doi.org\/10.1109\/TCBB.2017.2776910","journal-title":"IEEE\/ACM Trans Comput Biol Bioinforma"},{"key":"13506_CR87","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1007\/s13139-019-00610-0","volume":"53","author":"K J-Y","year":"2019","unstructured":"J-Y K, Suh HY, Ryoo HG et al (2019) Amyloid PET quantification via end-to-end training of a deep learning. Nucl Med Mol Imaging 53:340\u2013348","journal-title":"Nucl Med Mol Imaging"},{"key":"13506_CR88","doi-asserted-by":"crossref","unstructured":"Kang H, Kang D, Park J, Ha SW (2018) VGG19-based classification of amyloid PET image in patients with MCI and AD. In: 2018 international conference on computational science and computational intelligence (CSCI). Pp 1442\u20131443.","DOI":"10.1109\/CSCI46756.2018.00281"},{"key":"13506_CR89","first-page":"287","volume-title":"Nguyen NT","author":"H Karasawa","year":"2018","unstructured":"Karasawa H, Liu C-L, Ohwada H (2018) Deep 3D convolutional neural network architectures for Alzheimer\u2019s disease diagnosis BT - intelligent information and database systems. In: Hoang DH, Hong T-P et al (eds) Nguyen NT. Springer International Publishing, Cham, pp 287\u2013296"},{"key":"13506_CR90","first-page":"316","volume-title":"Ten Teije a","author":"A Karwath","year":"2017","unstructured":"Karwath A, Hubrich M, Kramer S (2017) Convolutional neural networks for the identification of regions of interest in PET scans: a study of representation learning for diagnosing Alzheimer\u2019s disease BT - artificial intelligence in Medicine. In: Popow C, Holmes JH, Sacchi L (eds) Ten Teije a. Springer International Publishing, Cham, pp 316\u2013321"},{"key":"13506_CR91","doi-asserted-by":"crossref","unstructured":"Kavitha M, Yudistira N, Kurita T (2019) Multi instance learning via deep CNN for multi-class recognition of Alzheimer\u2019s disease. In: 2019 IEEE 11th international workshop on computational intelligence and applications (IWCIA). Pp 89\u201394.","DOI":"10.1109\/IWCIA47330.2019.8955006"},{"key":"13506_CR92","doi-asserted-by":"crossref","unstructured":"Khagi B, Lee CG, Kwon G (2018) Alzheimer\u2019s disease classification from brain MRI based on transfer learning from CNN. In: 2018 11th biomedical engineering international conference (BMEiCON). Pp 1\u20134.","DOI":"10.1109\/BMEiCON.2018.8609974"},{"key":"13506_CR93","doi-asserted-by":"publisher","first-page":"2197","DOI":"10.1007\/s00259-019-04676-y","volume":"47","author":"HW Kim","year":"2020","unstructured":"Kim HW, Lee HE, Lee S, Oh KT, Yun M, Yoo SK (2020) Slice-selective learning for Alzheimer\u2019s disease classification using a generative adversarial network: a feasibility study of external validation. Eur J Nucl Med Mol Imaging 47:2197\u20132206. https:\/\/doi.org\/10.1007\/s00259-019-04676-y","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"13506_CR94","doi-asserted-by":"crossref","unstructured":"Kompanek M, Tamajka M, Benesova W (2019) Volumetrie data augmentation as an effective tool in MRI classification using 3D convolutional neural network. In: 2019 international conference on systems, signals and image processing (IWSSIP). Pp 115\u2013119.","DOI":"10.1109\/IWSSIP.2019.8787315"},{"key":"13506_CR95","doi-asserted-by":"crossref","unstructured":"Korolev S, Safiullin A, Belyaev M, Dodonova Y (2017) Residual and plain convolutional neural networks for 3D brain MRI classification. 2017 IEEE 14th Int Symp Biomed Imag (ISBI 2017). Pp 835\u2013838","DOI":"10.1109\/ISBI.2017.7950647"},{"key":"13506_CR96","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.imu.2018.12.001","volume":"14","author":"KR Kruthika","year":"2019","unstructured":"Kruthika KR, Rajeswari MHD (2019) CBIR system using capsule networks and 3D CNN for Alzheimer\u2019s disease diagnosis. Informatics Med Unlocked 14:59\u201368. https:\/\/doi.org\/10.1016\/j.imu.2018.12.001","journal-title":"Informatics Med Unlocked"},{"issue":"1","key":"13506_CR97","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-93658-y,14133","volume":"11","author":"R Kundu","year":"2021","unstructured":"Kundu R, Basak H, Singh PK, Ahmadian A, Ferrara M, Sarkar R (2021) Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans. Sci Rep 11(1):1\u201312. https:\/\/doi.org\/10.1038\/s41598-021-93658-y,14133","journal-title":"Sci Rep"},{"key":"13506_CR98","unstructured":"Lam P, Marcin J, Felman A (2018) What to know about MRI scans, 2018. Available at: https:\/\/www.medicalnewstoday.com\/articles\/146309.php. [Accessed: 10-Dec-2018]"},{"key":"13506_CR99","first-page":"320","volume-title":"Springer","author":"B Lemoine","year":"2010","unstructured":"Lemoine B, Rayburn S, Benton R (2010) Data fusion and feature selection for Alzheimer\u2019s diagnosis BT - brain informatics. In: Yao Y, Sun R, Poggio T et al (eds) Springer. Berlin Heidelberg, Berlin, Heidelberg, pp 320\u2013327"},{"key":"13506_CR100","doi-asserted-by":"crossref","unstructured":"Li F, Cheng D, Liu M (2017) Alzheimer\u2019s disease classification based on combination of multi-model convolutional networks. In: 2017 IEEE international conference on imaging systems and techniques (IST). Pp 1\u20135.","DOI":"10.1109\/IST.2017.8261566"},{"key":"13506_CR101","first-page":"519","volume-title":"Cong G","author":"X Li","year":"2017","unstructured":"Li X, Li Y, Li X (2017) Predicting clinical outcomes of Alzheimer\u2019s disease from complex brain networks BT - advanced data mining and applications. In: Peng W-C, Zhang WE et al (eds) Cong G. Springer International Publishing, Cham, pp 519\u2013525"},{"key":"13506_CR102","doi-asserted-by":"publisher","first-page":"880","DOI":"10.1109\/TPAMI.2018.2889096","volume":"42","author":"C Lian","year":"2020","unstructured":"Lian C, Liu M, Zhang J, Shen D (2020) Hierarchical fully convolutional network for joint atrophy localization and Alzheimer\u2019s disease diagnosis using structural MRI. IEEE Trans Pattern Anal Mach Intell 42:880\u2013893. https:\/\/doi.org\/10.1109\/TPAMI.2018.2889096","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3\u20134","key":"13506_CR103","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1007\/s12021-018-9370-4","volume":"16","author":"M Liu","year":"2018","unstructured":"Liu M, Cheng D, Wang K et al (2018) Multi-modality cascaded convolutional neural networks for Alzheimer\u2019s disease diagnosis. Neuroinformatics 16(3\u20134):295\u2013308. https:\/\/doi.org\/10.1007\/s12021-018-9370-4","journal-title":"Neuroinformatics"},{"key":"13506_CR104","doi-asserted-by":"publisher","first-page":"1195","DOI":"10.1109\/TBME.2018.2869989","volume":"66","author":"M Liu","year":"2019","unstructured":"Liu M, Zhang J, Adeli E, Shen D (2019) Joint classification and regression via deep multi-task Multi-Channel learning for Alzheimer\u2019s disease diagnosis. IEEE Trans Biomed Eng 66:1195\u20131206. https:\/\/doi.org\/10.1109\/TBME.2018.2869989","journal-title":"IEEE Trans Biomed Eng"},{"key":"13506_CR105","doi-asserted-by":"publisher","first-page":"1132","DOI":"10.1109\/TBME.2014.2372011","volume":"62","author":"S Liu","year":"2015","unstructured":"Liu S, Liu S, Cai W, Che H, Pujol S, Kikinis R, Feng D, Fulham MJ, ADNI (2015) Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer\u2019s disease. IEEE Trans Biomed Eng 62:1132\u20131140. https:\/\/doi.org\/10.1109\/TBME.2014.2372011","journal-title":"IEEE Trans Biomed Eng"},{"key":"13506_CR106","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.trsl.2018.01.001","volume":"194","author":"X Liu","year":"2018","unstructured":"Liu X, Chen K, Wu T, Weidman D, Lure F, Li J (2018) Use of multimodality imaging and artificial intelligence for diagnosis and prognosis of early stages of Alzheimer\u2019s disease. Transl Res 194:56\u201367. https:\/\/doi.org\/10.1016\/j.trsl.2018.01.001","journal-title":"Transl Res"},{"key":"13506_CR107","doi-asserted-by":"publisher","first-page":"1260","DOI":"10.1016\/j.neucom.2010.06.025","volume":"74","author":"M L\u00f3pez","year":"2011","unstructured":"L\u00f3pez M, Ram\u00edrez J, G\u00f3rriz JM, \u00c1lvarez I, Salas-Gonzalez D, Segovia F, Chaves R, Padilla P, G\u00f3mez-R\u00edo M (2011) Principal component analysis-based techniques and supervised classification schemes for the early detection of Alzheimer\u2019s disease. Neurocomputing 74:1260\u20131271. https:\/\/doi.org\/10.1016\/j.neucom.2010.06.025","journal-title":"Neurocomputing"},{"key":"13506_CR108","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.media.2018.02.002","volume":"46","author":"D Lu","year":"2018","unstructured":"Lu D, Popuri K, Ding GW, Balachandar R, Beg MF, Initiative A\u2019s DN (2018) Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer\u2019s disease. Med Image Anal 46:26\u201334. https:\/\/doi.org\/10.1016\/j.media.2018.02.002","journal-title":"Med Image Anal"},{"key":"13506_CR109","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/j.neunet.2012.02.035","volume":"32","author":"BS Mahanand","year":"2012","unstructured":"Mahanand BS, Suresh S, Sundararajan N, Aswatha Kumar M (2012) Identification of brain regions responsible for Alzheimer\u2019s disease using a self-adaptive resource allocation network. Neural Netw 32:313\u2013322. https:\/\/doi.org\/10.1016\/j.neunet.2012.02.035","journal-title":"Neural Netw"},{"key":"13506_CR110","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.procs.2019.12.089","volume":"163","author":"BF Marghalani","year":"2019","unstructured":"Marghalani BF, Arif M (2019) Automatic classification of brain tumor and Alzheimer\u2019s disease in MRI. Procedia Comput Sci 163:78\u201384. https:\/\/doi.org\/10.1016\/j.procs.2019.12.089","journal-title":"Procedia Comput Sci"},{"key":"13506_CR111","doi-asserted-by":"publisher","first-page":"9676","DOI":"10.1016\/j.eswa.2012.02.153","volume":"39","author":"FJ Mart\u00ednez-Murcia","year":"2012","unstructured":"Mart\u00ednez-Murcia FJ, G\u00f3rriz JM, Ram\u00edrez J, Puntonet CG, Salas-Gonz\u00e1lez D (2012) Computer aided diagnosis tool for Alzheimer\u2019s disease based on Mann\u2013Whitney\u2013Wilcoxon U-test. Expert Syst Appl 39:9676\u20139685. https:\/\/doi.org\/10.1016\/j.eswa.2012.02.153","journal-title":"Expert Syst Appl"},{"key":"13506_CR112","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez-Murcia FJ, G\u00f3rriz JM, Ram\u00edrez J et al (2018) A deep decomposition of MRI to explore neurodegeneration in Alzheimer\u2019s disease. IEEE nuclear science symposium and medical imaging conference proceedings (NSS\/MIC). Pp 1-3.","DOI":"10.1109\/NSSMIC.2018.8824320"},{"key":"13506_CR113","doi-asserted-by":"crossref","unstructured":"Mathew NA, Vivek RS, Anurenjan PR (2018) Early diagnosis of Alzheimer\u2019s disease from MRI images using PNN. In: 2018 international CET conference on control, communication, and computing (IC4). Pp 161\u2013164.","DOI":"10.1109\/CETIC4.2018.8530910"},{"key":"13506_CR114","doi-asserted-by":"crossref","unstructured":"Morabito FC, Campolo M, Ieracitano C, et al (2016) Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer\u2019s disease patients from scalp EEG recordings. In 2016 IEEE 2nd international forum on research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI). Pp. 1-6.","DOI":"10.1109\/RTSI.2016.7740576"},{"issue":"6","key":"13506_CR115","first-page":"578","volume":"3","author":"E Murphy","year":"1999","unstructured":"Murphy E, Galen BA (1999) What is a PET scan? Lippincott\u2019s Primary Care Pract 3(6):578\u2013580","journal-title":"Lippincott\u2019s Primary Care Pract"},{"key":"13506_CR116","unstructured":"Nearest neighbor. Retrived from https:\/\/towardsdatascience.com\/machine-learning-basics-with-the-k-nearest-neighbors-algorithm-6a6e71d01761(n.d.)"},{"issue":"4","key":"13506_CR117","doi-asserted-by":"publisher","first-page":"816","DOI":"10.1007\/s10278-020-00321-5","volume":"33","author":"KT Oh","year":"2020","unstructured":"Oh KT, Lee S, Lee H, Yun M, Yoo SK (2020) Semantic segmentation of white matter in FDG-PET using generative adversarial network. J Digit Imaging 33(4):816\u2013825. https:\/\/doi.org\/10.1007\/s10278-020-00321-5","journal-title":"J Digit Imaging"},{"key":"13506_CR118","first-page":"455","volume-title":"Frangi AF","author":"Y Pan","year":"2018","unstructured":"Pan Y, Liu M, Lian C et al (2018) Synthesizing missing PET from MRI with cycle-consistent generative adversarial networks for Alzheimer\u2019s disease diagnosis BT - medical image computing and computer assisted intervention \u2013 MICCAI 2018. In: Schnabel JA, Davatzikos C et al (eds) Frangi AF. Springer International Publishing, Cham, pp 455\u2013463"},{"key":"13506_CR119","first-page":"137","volume-title":"Shen D","author":"Y Pan","year":"2019","unstructured":"Pan Y, Liu M, Lian C et al (2019) Disease-image specific generative adversarial network for brain disease diagnosis with incomplete multi-modal Neuroimages BT - medical image computing and computer assisted intervention \u2013 MICCAI 2019. In: Liu T, Peters TM et al (eds) Shen D. Springer International Publishing, Cham, pp 137\u2013145"},{"key":"13506_CR120","first-page":"731","volume-title":"Pant M","author":"KC Pathak","year":"2020","unstructured":"Pathak KC, Kundaram SS (2020) Accuracy-based performance analysis of Alzheimer\u2019s disease classification using deep convolution neural network BT - soft computing: theories and applications. In: Kumar Sharma T, Arya R et al (eds) Pant M. Springer Singapore, Singapore, pp 731\u2013744"},{"issue":"1","key":"13506_CR121","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)IS.1943-555X.0000512","volume":"26","author":"SM Piryonesi","year":"2020","unstructured":"Piryonesi SM, El-Diraby T (2020) Data analytics in asset management: cost-effective prediction of the pavement condition. J Infrastruct Syst 26(1):04019036. https:\/\/doi.org\/10.1061\/(ASCE)IS.1943-555X.0000512","journal-title":"J Infrastruct Syst"},{"key":"13506_CR122","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1016\/j.neuroimage.2009.11.046","volume":"50","author":"C Plant","year":"2010","unstructured":"Plant C, Teipel SJ, Oswald A, B\u00f6hm C, Meindl T, Mourao-Miranda J, Bokde AW, Hampel H, Ewers M (2010) Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer\u2019s disease. Neuroimage 50:162\u2013174. https:\/\/doi.org\/10.1016\/j.neuroimage.2009.11.046","journal-title":"Neuroimage"},{"issue":"4","key":"13506_CR123","doi-asserted-by":"publisher","first-page":"1","DOI":"10.22034\/jbr.2020.251812.1035","volume":"2","author":"H Rafieipour","year":"2020","unstructured":"Rafieipour H, Abdollah Zadeh A, Moradan A, Salekshahrezaee A (2020) Study of genes associated with Parkinson disease using feature selection. J Bioeng Res 2(4):1\u201311. https:\/\/doi.org\/10.22034\/jbr.2020.251812.1035","journal-title":"J Bioeng Res"},{"issue":"3","key":"13506_CR124","first-page":"32","volume":"7","author":"H Rafieipour","year":"2020","unstructured":"Rafieipour H, Abdollah Zadeh A, Mirzae M (2020) Distributed frequent itemset mining with bitwise method and using the gossip-based protocol. J Soft Comput Decision Support Syst 7(3):32\u201339","journal-title":"J Soft Comput Decision Support Syst"},{"issue":"2","key":"13506_CR125","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-019-1475-2","volume":"44","author":"F Ramzan","year":"2020","unstructured":"Ramzan F, Khan MUG, Rehmat A, Iqbal S, Saba T, Rehman A, Mehmood Z (2020) A deep learning approach for automated diagnosis and multi-class classification of Alzheimer\u2019s disease stages using resting-state fMRI and residual neural networks. J Med Syst 44(2):1\u201316. https:\/\/doi.org\/10.1007\/s10916-019-1475-2","journal-title":"J Med Syst"},{"key":"13506_CR126","doi-asserted-by":"crossref","unstructured":"Raut A, Dalal V (2017) A machine learning based approach for detection of alzheimer\u2019s disease using analysis of hippocampus region from MRI scan. In: 2017 international conference on computing methodologies and communication (ICCMC). Pp 236\u2013242.","DOI":"10.1109\/ICCMC.2017.8282683"},{"issue":"3","key":"13506_CR127","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1038\/nrneurol.2011.2","volume":"7","author":"C Reitz","year":"2011","unstructured":"Reitz C, Brayne C, Mayeux R (2011) Epidemiology of Alzheimer disease. Nat Rev Neurol 7(3):137\u2013152","journal-title":"Nat Rev Neurol"},{"key":"13506_CR128","unstructured":"Ross H (2017) CT (computed tomography) scan. In: healthline. https:\/\/www.healthline.com\/health\/ct-scan."},{"issue":"1","key":"13506_CR129","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-020-00352-3","volume":"7","author":"M Rostami","year":"2020","unstructured":"Rostami M, Berahmand K, Forouzandeh SA (2020) A novel method of constrained feature selection by the measurement of pairwise constraints uncertainty. J Big Data 7(1):1\u201321. https:\/\/doi.org\/10.1186\/s40537-020-00352-3","journal-title":"J Big Data"},{"key":"13506_CR130","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-020-00398-3","volume":"8","author":"M Rostami","year":"2021","unstructured":"Rostami M, Berahmand K, Forouzandeh SA (2021) A novel community detection based genetic algorithm for feature selection. J Big Data 8:1\u201327. https:\/\/doi.org\/10.1186\/s40537-020-00398-3","journal-title":"J Big Data"},{"key":"13506_CR131","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis 115:211\u2013252. https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int J Comput Vis"},{"issue":"1","key":"13506_CR132","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-87523-1","volume":"11","author":"P Saha","year":"2021","unstructured":"Saha P, Mukherjee D, Singh PK, Ahmadian A, Ferrara M, Sarkar R (2021) GraphCovidNet: a graph neural network based model for detecting COVID-19 from CT scans and X-rays of chest. Sci Rep 11(1):1\u201316. https:\/\/doi.org\/10.1038\/s41598-021-87523-1","journal-title":"Sci Rep"},{"key":"13506_CR133","doi-asserted-by":"crossref","unstructured":"Sahumbaiev I, Popov A, Ivanushkina N et al (2018) Florbetapir image analysis for Alzheimer\u2019s disease diagnosis. In: 2018 IEEE 38th international conference on electronics and nanotechnology (ELNANO). Pp 277\u2013280.","DOI":"10.1109\/ELNANO.2018.8477516"},{"key":"13506_CR134","doi-asserted-by":"crossref","unstructured":"Saraswathi S, Mahanand BS, Kloczkowski A et al (2013) Detection of onset of Alzheimer\u2019s disease from MRI images using a GA-ELM-PSO classifier. In: 2013 fourth international workshop on computational intelligence in medical imaging (CIMI). Pp 42\u201348","DOI":"10.1109\/CIMI.2013.6583856"},{"key":"13506_CR135","doi-asserted-by":"crossref","unstructured":"Sarraf S, Tofighi G (2016) Deep learning-based pipeline to recognize Alzheimer\u2019s disease using fMRI data. In: 2016 future technologies conference (FTC). Pp 816\u2013820.","DOI":"10.1109\/FTC.2016.7821697"},{"key":"13506_CR136","first-page":"169","volume-title":"Chen Y-W","author":"R Sato","year":"2019","unstructured":"Sato R, Iwamoto Y, Cho K et al (2019) Comparison of CNN models with different plane images and their combinations for classification of Alzheimer\u2019s disease using PET images BT - innovation in Medicine and healthcare systems, and multimedia. In: Zimmermann A, Howlett RJ, Jain LC (eds) Chen Y-W. Springer Singapore, Singapore, pp 169\u2013177"},{"key":"13506_CR137","doi-asserted-by":"crossref","unstructured":"Segovia F, Phillips C (2014) PET imaging analysis using a parcelation approach and multiple kernel classification. In: 2014 international workshop on pattern recognition in neuroimaging pp 1\u20134.","DOI":"10.1109\/PRNI.2014.6858544"},{"key":"13506_CR138","doi-asserted-by":"publisher","unstructured":"Seliya N, Abdollah Zadeh A, Khoshgoftaar TM (2021) A literature review on one-class classification and its potential applications in big data. J Big Data 8(122). https:\/\/doi.org\/10.1186\/s40537-021-00514-x","DOI":"10.1186\/s40537-021-00514-x"},{"key":"13506_CR139","doi-asserted-by":"publisher","first-page":"164237","DOI":"10.1016\/j.ijleo.2020.164237","volume":"212","author":"A Shakarami","year":"2020","unstructured":"Shakarami A, Tarrah H, Mahdavi-Hormat A (2020) A CAD system for diagnosing Alzheimer\u2019s disease using 2D slices and an improved AlexNet-SVM method. Optik (Stuttg) 212:164237. https:\/\/doi.org\/10.1016\/j.ijleo.2020.164237","journal-title":"Optik (Stuttg)"},{"key":"13506_CR140","first-page":"15","volume-title":"Reuter M","author":"M Shakeri","year":"2016","unstructured":"Shakeri M, Lombaert H, Tripathi S, Kadoury S (2016) Deep spectral-based shape features for Alzheimer\u2019s disease classification BT - spectral and shape analysis in medical imaging. In: Wachinger C, Lombaert H (eds) Reuter M. Springer International Publishing, Cham, pp 15\u201324"},{"key":"13506_CR141","doi-asserted-by":"crossref","unstructured":"Shen T, Jiang J, Li Y et al (2018) Decision supporting model for one-year conversion Probability from MCI to AD using CNN and SVM. In: 2018 40th annual international conference of the IEEE engineering in Medicine and biology society (EMBC). Pp 738\u2013741.","DOI":"10.1109\/EMBC.2018.8512398"},{"key":"13506_CR142","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1016\/j.patcog.2016.09.032","volume":"63","author":"B Shi","year":"2017","unstructured":"Shi B, Chen Y, Zhang P, Smith CD, Liu J (2017) Nonlinear feature transformation and deep fusion for Alzheimer\u2019s disease staging analysis. Pattern Recogn 63:487\u2013498. https:\/\/doi.org\/10.1016\/j.patcog.2016.09.032","journal-title":"Pattern Recogn"},{"key":"13506_CR143","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1109\/JBHI.2017.2655720","volume":"22","author":"J Shi","year":"2018","unstructured":"Shi J, Zheng X, Li Y, Zhang Q, Ying S (2018) Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer\u2019s disease. IEEE J Biomed Heal Inform 22:173\u2013183. https:\/\/doi.org\/10.1109\/JBHI.2017.2655720","journal-title":"IEEE J Biomed Heal Inform"},{"key":"13506_CR144","doi-asserted-by":"crossref","unstructured":"Silva IRR, Silva GSL, de Souza RG et al (2019) Model based on deep feature extraction for diagnosis of Alzheimer\u2019s disease. In: 2019 international joint conference on neural networks (IJCNN). Pp 1\u20137","DOI":"10.1109\/IJCNN.2019.8852138"},{"key":"13506_CR145","doi-asserted-by":"crossref","unstructured":"Simon BC, Baskar D, Jayanthi VS (2019) Alzheimer\u2019s disease classification using deep convolutional neural network. In: 2019 9th international conference on advances in computing and communication (ICACC). Pp 204\u2013208.","DOI":"10.1109\/ICACC48162.2019.8986170"},{"key":"13506_CR146","first-page":"3","volume-title":"Jmaiel M","author":"B Solano-Rojas","year":"2020","unstructured":"Solano-Rojas B, Villal\u00f3n-Fonseca R, Mar\u00edn-Ravent\u00f3s G (2020) Alzheimer\u2019s disease early detection using a low cost three-dimensional Densenet-121 architecture BT - the impact of digital technologies on public health in developed and developing countries. In: Mokhtari M, Abdulrazak B et al (eds) Jmaiel M. Springer International Publishing, Cham, pp 3\u201315"},{"key":"13506_CR147","doi-asserted-by":"crossref","unstructured":"Song T, Chowdhury SR, Yang F et al (2019) Graph convolutional neural networks for Alzheimer\u2019s disease classification. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019). Pp 414\u2013417.","DOI":"10.1109\/ISBI.2019.8759531"},{"key":"13506_CR148","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.neuroimage.2019.01.031","volume":"189","author":"S Spasov","year":"2019","unstructured":"Spasov S, Passamonti L, Duggento A, Li\u00f2 P, Toschi N, Initiative A's DN (2019) A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer\u2019s disease. Neuroimage 189:276\u2013287. https:\/\/doi.org\/10.1016\/j.neuroimage.2019.01.031","journal-title":"Neuroimage"},{"key":"13506_CR149","first-page":"203","volume-title":"Deep learning in diagnosis of brain disorders BT - recent Progress in brain and cognitive engineering","author":"H-I Suk","year":"2015","unstructured":"Suk H-I, Shen D (2015) Deep learning in diagnosis of brain disorders BT - recent Progress in brain and cognitive engineering. In: B\u00fclthoff HH, M\u00fcller K-R Lee S-W (eds) Springer Netherlands, Dordrecht, pp 203\u2013213"},{"key":"13506_CR150","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1007\/s00429-013-0687-3","volume":"220","author":"H-I Suk","year":"2015","unstructured":"Suk H-I, Lee S-W, Shen D, Initiative TADN (2015) Latent feature representation with stacked auto-encoder for AD\/MCI diagnosis. Brain Struct Funct 220:841\u2013859. https:\/\/doi.org\/10.1007\/s00429-013-0687-3","journal-title":"Brain Struct Funct"},{"key":"13506_CR151","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, et al (2015) Going deeper with convolutions. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR). Pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"13506_CR152","doi-asserted-by":"crossref","unstructured":"Tabarestani S, Aghili M, Shojaie M et al (2019) Longitudinal prediction modeling of Alzheimer disease using recurrent neural networks. In: 2019 IEEE EMBS international conference on Biomedical & Health Informatics (BHI). Pp 1\u20134.","DOI":"10.1109\/BHI.2019.8834556"},{"key":"13506_CR153","doi-asserted-by":"publisher","first-page":"6825","DOI":"10.1007\/s00500-018-3421-5","volume":"22","author":"V T-d","year":"2018","unstructured":"T-d V, N-h H, H-j Y et al (2018) Non-white matter tissue extraction and deep convolutional neural network for Alzheimer\u2019s disease detection. Soft Comput 22:6825\u20136833. https:\/\/doi.org\/10.1007\/s00500-018-3421-5","journal-title":"Soft Comput"},{"key":"13506_CR154","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-70911-1_20","volume-title":"Brain theory","author":"C Van Der Malsburg","year":"1986","unstructured":"Van Der Malsburg C (1986) Frank Rosenblatt: principles of Neurodynamics: Perceptrons and the theory of brain mechanisms. In: Palm G, Aertsen A (eds) Brain theory. Springer, Berlin, Heidelberg. https:\/\/doi.org\/10.1007\/978-3-642-70911-1_20"},{"key":"13506_CR155","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1016\/j.neuroimage.2012.09.015","volume":"64","author":"R Vandenberghe","year":"2013","unstructured":"Vandenberghe R, Nelissen N, Salmon E, Ivanoiu A, Hasselbalch S, Andersen A, Korner A, Minthon L, Brooks DJ, van Laere K, Dupont P (2013) Binary classification of 18F-flutemetamol PET using machine learning: comparison with visual reads and structural MRI. Neuroimage 64:517\u2013525. https:\/\/doi.org\/10.1016\/j.neuroimage.2012.09.015","journal-title":"Neuroimage"},{"key":"13506_CR156","doi-asserted-by":"crossref","unstructured":"Vinutha N, Pattar S, Kumar C et al (2018) A convolution neural network based classifier for diagnosis of Alzheimer\u2019s disease. In: 2018 fourteenth international conference on information processing (ICINPRO). Pp 1\u20136.","DOI":"10.1109\/ICINPRO43533.2018.9096819"},{"key":"13506_CR157","doi-asserted-by":"crossref","unstructured":"Vu TD, Yang H, Nguyen VQ et al (2017) Multimodal learning using convolution neural network and sparse autoencoder. In: 2017 IEEE international conference on big data and smart computing (BigComp). Pp 309\u2013312.","DOI":"10.1109\/BIGCOMP.2017.7881683"},{"key":"13506_CR158","doi-asserted-by":"publisher","first-page":"219","DOI":"10.2463\/mrms.mp.2018-0091","volume":"18","author":"A Wada","year":"2019","unstructured":"Wada A, Tsuruta K, Irie R, Kamagata K, Maekawa T, Fujita S, Koshino S, Kumamaru K, Suzuki M, Nakanishi A, Hori M, Aoki S (2019) Differentiating Alzheimer\u2019s disease from dementia with Lewy bodies using a deep learning technique based on structural brain connectivity. Magn Reson Med Sci 18:219\u2013224. https:\/\/doi.org\/10.2463\/mrms.mp.2018-0091","journal-title":"Magn Reson Med Sci"},{"key":"13506_CR159","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.neucom.2018.12.018","volume":"333","author":"H Wang","year":"2019","unstructured":"Wang H, Shen Y, Wang S, Xiao T, Deng L, Wang X, Zhao X (2019) Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer\u2019s disease. Neurocomputing 333:145\u2013156. https:\/\/doi.org\/10.1016\/j.neucom.2018.12.018","journal-title":"Neurocomputing"},{"issue":"5","key":"13506_CR160","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-018-0932-7","volume":"42","author":"S-H Wang","year":"2018","unstructured":"Wang S-H, Phillips P, Sui Y, Liu B, Yang M, Cheng H (2018) Classification of Alzheimer\u2019s disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling. J Med Syst 42(5):1\u201311. https:\/\/doi.org\/10.1007\/s10916-018-0932-7","journal-title":"J Med Syst"},{"key":"13506_CR161","doi-asserted-by":"crossref","unstructured":"Wang Y, Yang Y, Guo X et al (2018) A novel multimodal MRI analysis for Alzheimer\u2019s disease based on convolutional neural network. In: 2018 40th annual international conference of the IEEE engineering in Medicine and biology society (EMBC). Pp 754\u2013757.","DOI":"10.1109\/EMBC.2018.8512372"},{"key":"13506_CR162","first-page":"128","volume-title":"Springer","author":"Y Xia","year":"2012","unstructured":"Xia Y, Zhang Z, Wen L et al (2012) GA and AdaBoost-based feature selection and combination for automated identification of dementia using FDG-PET imaging BT - intelligent science and intelligent data engineering. In: Zhang Y, Zhou Z-H, Zhang C, Li Y (eds) Springer. Berlin Heidelberg, Berlin, Heidelberg, pp 128\u2013135"},{"key":"13506_CR163","doi-asserted-by":"crossref","unstructured":"Xia Z, Yue G, Xu Y et al (2020) A novel end-to-end hybrid network for Alzheimer\u2019s disease detection using 3D CNN and 3D CLSTM. In: 2020 IEEE 17th international symposium on biomedical imaging (ISBI). Pp 1\u20134.","DOI":"10.1109\/ISBI45749.2020.9098621"},{"key":"13506_CR164","doi-asserted-by":"crossref","unstructured":"Xu M, Liu Z, Wang Z et al (2019) The diagnosis of Alzheimer\u2019s disease based on enhanced residual neutral network. In: 2019 international conference on cyber-enabled distributed computing and knowledge discovery (CyberC). Pp 405\u2013411.","DOI":"10.1109\/CyberC.2019.00076"},{"key":"13506_CR165","first-page":"26","volume-title":"Rekik I","author":"Y Yan","year":"2018","unstructured":"Yan Y, Lee H, Somer E, Grau V (2018) Generation of amyloid PET images via conditional adversarial training for predicting progression to Alzheimer\u2019s disease BT - PRedictive intelligence in MEdicine. In: Unal G, Adeli E, Park SH (eds) Rekik I. Springer International Publishing, Cham, pp 26\u201333"},{"key":"13506_CR166","unstructured":"Yang C, Rangarajan A, Ranka S (2018) Visual Explanations From Deep 3D Convolutional Neural Networks for Alzheimer\u2019s Disease Classification. AMIA . Annu Symp proceedings AMIA Symp 2018: pp. 1571\u20131580."},{"key":"13506_CR167","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1016\/j.sjbs.2019.12.004","volume":"27","author":"Z Yang","year":"2020","unstructured":"Yang Z, Liu Z (2020) The risk prediction of Alzheimer\u2019s disease based on the deep learning model of brain 18F-FDG positron emission tomography. Saudi J Biol Sci 27:659\u2013665. https:\/\/doi.org\/10.1016\/j.sjbs.2019.12.004","journal-title":"Saudi J Biol Sci"},{"key":"13506_CR168","doi-asserted-by":"publisher","first-page":"597","DOI":"10.3938\/jkps.75.597","volume":"75","author":"HJ Yoon","year":"2019","unstructured":"Yoon HJ, Jeong YJ, Kang D-Y, Kang H, Yeo KK, Jeong JE, Park KW, Choi GE, Ha SW (2019) Effect of data augmentation of F-18-Florbetaben positron-emission tomography images by using deep learning convolutional neural network architecture for amyloid positive patients. J Korean Phys Soc 75:597\u2013604. https:\/\/doi.org\/10.3938\/jkps.75.597","journal-title":"J Korean Phys Soc"},{"key":"13506_CR169","doi-asserted-by":"crossref","unstructured":"Yue L, Gong X, Chen K et al (2018) Auto-detection of Alzheimer\u2019s disease using deep convolutional neural networks. In: 2018 14th international conference on natural computation, fuzzy systems and knowledge discovery (ICNC-FSKD). Pp 228\u2013234","DOI":"10.1109\/FSKD.2018.8687207"},{"key":"13506_CR170","unstructured":"Zeiler MD (2013) Hierarchical convolutional deep learning in computer vision. New York University. ProQuest dissertations publishing, 3614917."},{"key":"13506_CR171","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.neucom.2019.04.093","volume":"361","author":"F Zhang","year":"2019","unstructured":"Zhang F, Li Z, Zhang B, Du H, Wang B, Zhang X (2019) Multi-modal deep learning model for auxiliary diagnosis of Alzheimer\u2019s disease. Neurocomputing 361:185\u2013195. https:\/\/doi.org\/10.1016\/j.neucom.2019.04.093","journal-title":"Neurocomputing"},{"key":"13506_CR172","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/s11682-011-9142-3","volume":"6","author":"J Zhang","year":"2012","unstructured":"Zhang J, Yu C, Jiang G, Liu W, Tong L (2012) 3D texture analysis on MRI images of Alzheimer\u2019s disease. Brain Imaging Behav 6:61\u201369. https:\/\/doi.org\/10.1007\/s11682-011-9142-3","journal-title":"Brain Imaging Behav"},{"key":"13506_CR173","doi-asserted-by":"publisher","first-page":"108795","DOI":"10.1016\/j.jneumeth.2020.108795","volume":"341","author":"T Zhang","year":"2020","unstructured":"Zhang T, Shi M (2020) Multi-modal neuroimaging feature fusion for diagnosis of Alzheimer\u2019s disease. J Neurosci Methods 341:108795. https:\/\/doi.org\/10.1016\/j.jneumeth.2020.108795","journal-title":"J Neurosci Methods"},{"issue":"10","key":"13506_CR174","doi-asserted-by":"publisher","first-page":"1943","DOI":"10.1109\/TPAMI.2015.2502579","volume":"38","author":"X Zhang","year":"2016","unstructured":"Zhang X, Zou J, He K, Sun J (2016) Accelerating very deep convolutional networks for classification and detection. IEEE Trans Pattern Anal Mach Intell 38(10):1943\u20131955. https:\/\/doi.org\/10.1109\/TPAMI.2015.2502579","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"13506_CR175","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.bspc.2015.05.014","volume":"21","author":"Y Zhang","year":"2015","unstructured":"Zhang Y, Wang S, Phillips P, Dong Z, Ji G, Yang J (2015) Detection of Alzheimer\u2019s disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA-KSVM trained by PSOTVAC. Biomed Signal Process Control 21:58\u201373. https:\/\/doi.org\/10.1016\/j.bspc.2015.05.014","journal-title":"Biomed Signal Process Control"},{"key":"13506_CR176","first-page":"614","volume-title":"Peng Y","author":"C Zheng","year":"2018","unstructured":"Zheng C, Xia Y, Chen Y et al (2018) Early diagnosis of Alzheimer\u2019s disease by ensemble deep learning using FDG-PET BT - intelligence science and big data engineering. In: Yu K, Lu J, Jiang X (eds) Peng Y. Springer International Publishing, Cham, pp 614\u2013622"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-13506-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-13506-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-13506-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T01:06:12Z","timestamp":1664499972000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-13506-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,16]]},"references-count":176,"journal-issue":{"issue":"26","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["13506"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-13506-7","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,16]]},"assertion":[{"value":"13 February 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 October 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 July 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 August 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}