{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T12:30:30Z","timestamp":1765369830370},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2021,4,30]],"date-time":"2021-04-30T00:00:00Z","timestamp":1619740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,4,30]],"date-time":"2021-04-30T00:00:00Z","timestamp":1619740800000},"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":[[2021,7]]},"DOI":"10.1007\/s11042-021-10928-7","type":"journal-article","created":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T08:24:38Z","timestamp":1620030278000},"page":"26411-26428","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["A CAD system design to diagnosize alzheimers disease from MRI brain images using optimal deep neural network"],"prefix":"10.1007","volume":"80","author":[{"given":"Pemmu","family":"Raghavaiah","sequence":"first","affiliation":[]},{"given":"S.","family":"Varadarajan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,30]]},"reference":[{"issue":"4","key":"10928_CR1","doi-asserted-by":"publisher","first-page":"1249","DOI":"10.1007\/s11042-014-2123-y","volume":"74","author":"OB Ahmed","year":"2015","unstructured":"Ahmed OB, Benois-Pineau J, Allard M, Amar CB, Catheline G, Alzheimer\u2019s Disease Neuroimaging Initiative (2015) Classification of Alzheimer\u2019s disease subjects from MRI using hippocampal visual features. Multimed Tools Appl 74(4):1249\u20131266","journal-title":"Multimed Tools Appl"},{"key":"10928_CR2","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.bspc.2018.02.019","volume":"43","author":"T Altaf","year":"2018","unstructured":"Altaf T, Anwar SM, Gul N, Majeed MN, Majid M (2018) Multi-class Alzheimer\u2019s disease classification using image and clinical features. Biomed Sig Process Control 43:64\u201374","journal-title":"Biomed Sig Process Control"},{"issue":"2","key":"10928_CR3","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1016\/j.jalz.2018.09.009","volume":"15","author":"GM Babulal","year":"2019","unstructured":"Babulal GM, Quiroz YT, Albensi BC, Arenaza-Urquijo E, Astell AJ, Babiloni C, Bahar-Fuchs A et al (2019) Perspectives on ethnic and racial disparities in Alzheimer\u2019s disease and related dementias: Update and areas of immediate need. Alzheimer\u2019s Dement 15(2):292\u2013312","journal-title":"Alzheimer\u2019s Dement"},{"key":"10928_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.pscychresns.2019.01.014","volume-title":"Brain volumes and their ratios in Alzheimer\u00b4 s disease on magnetic resonance imaging segmented using Freesurfer 6.0","author":"A Bartos","year":"2019","unstructured":"Bartos A, Gregus D, Ibrahim I, Tint\u011bra J (2019) Brain volumes and their ratios in Alzheimer\u00b4 s disease on magnetic resonance imaging segmented using Freesurfer 6.0. Neuroimaging, Psychiatry Research"},{"key":"10928_CR5","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.neubiorev.2018.06.019","volume":"97","author":"AC Bilderbeck","year":"2019","unstructured":"Bilderbeck AC, Penninx BWJH, Arango C, van der Wee N, Kahn R, Rossum I W-v, Hayen A, Kas MJ, Post A, Dawson GR (2019) Overview of the clinical implementation of a study exploring social withdrawal in patients with schizophrenia and Alzheimer\u2019s disease. Neurosci Biobehav Rev 97:87\u201393","journal-title":"Neurosci Biobehav Rev"},{"issue":"1","key":"10928_CR6","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/s10479-017-2405-7","volume":"258","author":"A \u00c7evik","year":"2017","unstructured":"\u00c7evik A, Weber G-W, Ey\u00fcbo\u011flu BM, O\u011fuz KK, Alzheimer\u2019s Disease Neuroimaging Initiative (2017) Voxel-MARS: a method for early detection of Alzheimer\u2019s disease by classification of structural brain MRI. Ann Oper Res 258(1):31\u201357","journal-title":"Ann Oper Res"},{"key":"10928_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compmedimag.2019.01.005","volume":"73","author":"R Cui","year":"2019","unstructured":"Cui R, Liu M, Initiative A's DN (2019) RNN-based longitudinal analysis for diagnosis of Alzheimer\u2019s disease. Comput Med Imaging Graph 73:1\u201310","journal-title":"Comput Med Imaging Graph"},{"key":"10928_CR8","doi-asserted-by":"crossref","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\u00a057:147\u2013159","DOI":"10.1016\/j.cogsys.2018.12.015"},{"key":"10928_CR9","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.swevo.2018.02.013","volume":"44","author":"M Jain","year":"2019","unstructured":"Jain M, Singh V, Rani A (2019) A novel nature-inspired algorithm for optimization: squirrel search algorithm. Swarm Evol Comput 44:148\u2013175","journal-title":"Swarm Evol Comput"},{"issue":"1","key":"10928_CR10","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, Pan Z, Li Q (2019) Early diagnosis of Alzheimer\u2019s disease based on resting-state brain networks and deep learning. IEEE\/ACM Trans Comput Biol Bioinf (TCBB) 16(1):244\u2013257","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf (TCBB)"},{"issue":"1","key":"10928_CR11","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1002\/ima.22300","volume":"29","author":"V Karami","year":"2019","unstructured":"Karami V, Nittari G, Amenta F (2019) Neuroimaging computer-aided diagnosis systems for Alzheimer\u2019s disease. Int J Imaging Syst Technol 29(1):83\u201394","journal-title":"Int J Imaging Syst Technol"},{"key":"10928_CR12","doi-asserted-by":"crossref","unstructured":"Keserwani P, Pammi V S C, Prakash O, Khare A, Jeon M (2016) Classification of Alzheimer Disease using Gabor Texture Feature of Hippocampus Region. Int J Image Graph Sig Process 8, no. 6","DOI":"10.5815\/ijigsp.2016.06.02"},{"key":"10928_CR13","doi-asserted-by":"publisher","first-page":"3393","DOI":"10.1109\/TBME.2019.2904702","volume":"66","author":"H-C Li","year":"2019","unstructured":"Li H-C, Chen P-Y, Cheng H-F, Kuo Y-M, Huang C-C (2019) In vivo visualization of brain vasculature in Alzheimer\u2019s disease mice by high-frequency micro-Doppler imaging. IEEE Trans Biomed Eng 66:3393\u20133401","journal-title":"IEEE Trans Biomed Eng"},{"key":"10928_CR14","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.compmedimag.2018.09.009","volume":"70","author":"F Li","year":"2018","unstructured":"Li F, Liu M, Initiative A's DN (2018) Alzheimer\u2019s disease diagnosis based on multiple cluster dense convolutional networks. Comput Med Imaging Graph 70:101\u2013110","journal-title":"Comput Med Imaging Graph"},{"key":"10928_CR15","doi-asserted-by":"publisher","first-page":"101680","DOI":"10.1016\/j.nicl.2019.101680","volume":"22","author":"S-Y Lin","year":"2019","unstructured":"Lin S-Y, Lin C-P, Hsieh T-J, Lin C-F, Chen S-H, Chao Y-P, Chen Y-S, Hsu C-C, Kuo L-W (2019) Multiparametric graph theoretical analysis reveals altered structural and functional network topology in Alzheimer\u2019s disease. NeuroImage: Clinical 22:101680","journal-title":"NeuroImage: Clinical"},{"issue":"2","key":"10928_CR16","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1109\/TCBB.2016.2635144","volume":"15","author":"J Liu","year":"2016","unstructured":"Liu J, Li M, Lan W, Wu F-X, Pan Y, Wang J (2016) Classification of alzheimer's disease using whole brain hierarchical network. IEEE\/ACM Trans Comput Biol Bioinforma 15(2):624\u2013632","journal-title":"IEEE\/ACM Trans Comput Biol Bioinforma"},{"issue":"6","key":"10928_CR17","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1109\/TNB.2017.2707139","volume":"16","author":"J Liu","year":"2017","unstructured":"Liu J, Wang J, Hu B, Wu F-X, Pan Y (2017) Alzheimer\u2019s disease classification based on individual hierarchical networks constructed with 3-D texture features. IEEE Trans Nanobiosci 16(6):428\u2013437","journal-title":"IEEE Trans Nanobiosci"},{"key":"10928_CR18","doi-asserted-by":"crossref","unstructured":"Liu M, Zhang J, Adeli E, Shen D (2018) Joint classification and regression via deep multi-task Multi-Channel learning for Alzheimer's disease diagnosis. IEEE Trans Biomed Eng\u00a066(5):1195\u20131206","DOI":"10.1109\/TBME.2018.2869989"},{"key":"10928_CR19","doi-asserted-by":"crossref","unstructured":"Liu M, Zhang J, Lian C, Shen D (2019) Weakly supervised deep learning for brain disease prognosis using MRI and incomplete clinical scores. IEEE Trans Cybern:1\u201312","DOI":"10.1109\/TCYB.2019.2904186"},{"issue":"3","key":"10928_CR20","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1080\/13825585.2018.1475002","volume":"26","author":"SRA Meyer","year":"2019","unstructured":"Meyer SRA, De Jonghe JFM, Schmand B, Ponds RWHM (2019) Visual associations to retrieve episodic memory across healthy elderly, mild cognitive impairment, and patients with Alzheimer\u2019s disease. Aging Neuropsychol Cognit 26(3):447\u2013462","journal-title":"Aging Neuropsychol Cognit"},{"key":"10928_CR21","doi-asserted-by":"crossref","unstructured":"Pandya MD, Shah PD, Jardosh S (2019) Medical image diagnosis for disease detection: A deep learning approach. In U-Healthcare Monitoring Systems, pp. 37\u201360. Academic Press","DOI":"10.1016\/B978-0-12-815370-3.00003-7"},{"key":"10928_CR22","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1016\/j.patcog.2018.11.027","volume":"88","author":"J Peng","year":"2019","unstructured":"Peng J, Zhu X, Wang Y, An L, Shen D (2019) Structured sparsity regularized multiple kernel learning for Alzheimer\u2019s disease diagnosis. Pattern Recogn 88:370\u2013382","journal-title":"Pattern Recogn"},{"issue":"5","key":"10928_CR23","doi-asserted-by":"publisher","first-page":"1666","DOI":"10.1002\/hbm.24478","volume":"40","author":"C Platero","year":"2019","unstructured":"Platero C, L\u00f3pez ME, del Carmen Tobar M, Yus M, Maestu F (2019) Discriminating Alzheimer's disease progression using a new hippocampal marker from T1-weighted MRI: The local surface roughness. Hum Brain Mapp 40(5):1666\u20131676","journal-title":"Hum Brain Mapp"},{"issue":"1","key":"10928_CR24","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1186\/s40537-019-0190-7","volume":"6","author":"F Razavi","year":"2019","unstructured":"Razavi F, Tarokh MJ, Alborzi M (2019) An intelligent Alzheimer\u2019s disease diagnosis method using unsupervised feature learning. J Big Data 6(1):32","journal-title":"J Big Data"},{"issue":"3","key":"10928_CR25","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1007\/s10916-018-1147-7","volume":"43","author":"S Saravanakumar","year":"2019","unstructured":"Saravanakumar S, Thangaraj P (2019) A computer aided diagnosis system for identifying Alzheimer\u2019s from MRI scan using improved Adaboost. J Med Syst 43(3):76","journal-title":"J Med Syst"},{"key":"10928_CR26","doi-asserted-by":"crossref","unstructured":"Shi Y, Suk H-I, Yang G, Lee S-W, Shen D (2019) Leveraging coupled interaction for multimodal Alzheimer's disease diagnosis. IEEE Trans Neural Netw Learn Syst","DOI":"10.1109\/TNNLS.2019.2900077"},{"key":"10928_CR27","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","journal-title":"Neurocomputing"},{"key":"10928_CR28","unstructured":"Wang S-H, Zhang Y, Li Y-J, Jia W-J, Liu F-Y, Yang M-M, Zhang Y-D (2018) Single slice based detection for Alzheimer\u2019s disease via wavelet entropy and multilayer perceptron trained by biogeography-based optimization. Multimed Tools Appl:1\u201325"},{"key":"10928_CR29","doi-asserted-by":"publisher","first-page":"26157","DOI":"10.1109\/ACCESS.2019.2894530","volume":"7","author":"L Xu","year":"2019","unstructured":"Xu L, Yao Z, Li J, Lv C, Zhang H, Bin H (2019) Sparse feature learning with label information for Alzheimer\u2019s disease classification based on magnetic resonance imaging. IEEE Access 7:26157\u201326167","journal-title":"IEEE Access"},{"issue":"3","key":"10928_CR30","doi-asserted-by":"publisher","first-page":"855","DOI":"10.3233\/JAD-170069","volume":"65","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, Wang S, Sui Y, Yang M, Liu B, Cheng H, Sun J, Jia W, Phillips P, Gorriz JM (2018) Multivariate approach for Alzheimer\u2019s disease detection using stationary wavelet entropy and predator-prey particle swarm optimization. J Alzheimers Dis 65(3):855\u2013869","journal-title":"J Alzheimers Dis"},{"key":"10928_CR31","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.engappai.2016.01.032","volume":"50","author":"Y Zhang","year":"2016","unstructured":"Zhang Y, Zhang E, Chen W (2016) Deep neural network for halftone image classification based on sparse auto-encoder. Eng Appl Artif Intell 50:245\u2013255","journal-title":"Eng Appl Artif Intell"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-10928-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-021-10928-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-021-10928-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T06:11:32Z","timestamp":1672035092000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-021-10928-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,30]]},"references-count":31,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2021,7]]}},"alternative-id":["10928"],"URL":"https:\/\/doi.org\/10.1007\/s11042-021-10928-7","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,30]]},"assertion":[{"value":"9 October 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 April 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 April 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}