{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T19:20:50Z","timestamp":1768677650745,"version":"3.49.0"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T00:00:00Z","timestamp":1684368000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T00:00:00Z","timestamp":1684368000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-023-01853-7","type":"journal-article","created":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T10:02:01Z","timestamp":1684404121000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Alz-ConvNets for Classification of Alzheimer Disease Using Transfer Learning Approach"],"prefix":"10.1007","volume":"4","author":[{"given":"Amar","family":"Shukla","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rajeev","family":"Tiwari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shamik","family":"Tiwari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,18]]},"reference":[{"key":"1853_CR1","doi-asserted-by":"crossref","unstructured":"Shahbaz, M., Ali, S., Guergachi, A., Niazi, A., & Umer, A. Classification of Alzheimer's Disease using Machine Learning Techniques. In Data (pp. 296\u2013303). 2019.","DOI":"10.5220\/0007949902960303"},{"issue":"12","key":"1853_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2020.e05652","volume":"6","author":"K Aderghal","year":"2020","unstructured":"Aderghal K, Afdel K, Benois-Pineau J, et al. Improving Alzheimer\u2019s stage categorization with convolutional neural network using transfer learning and different magnetic resonance imaging modalities. Heliyon. 2020;6(12): e05652.","journal-title":"Heliyon"},{"key":"1853_CR3","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, et al. Automated classification of AD and mild cognitive impairment using a single MRI and deep neural networks. NeuroImage Clin. 2019;21:101645.","journal-title":"NeuroImage Clin"},{"key":"1853_CR4","doi-asserted-by":"crossref","unstructured":"Li H, Fan Y. Early prediction of AD dementia based on baseline hippocampal MRI and 1-year follow-up cognitive measures using deep recurrent neural networks. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019); 2019. p. 368\u2013371.","DOI":"10.1109\/ISBI.2019.8759397"},{"key":"1853_CR5","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2020.576194","author":"L Nanni","year":"2020","unstructured":"Nanni L, Interlenghi M, Brahnam S, et al. Comparison of transfer learning and conventional ml applied to structural brain MRI for the early diagnosis and prognosis of Alzheimer\u2019s disease. Front Neurol. 2020. https:\/\/doi.org\/10.3389\/fneur.2020.576194.","journal-title":"Front Neurol"},{"key":"1853_CR6","doi-asserted-by":"crossref","unstructured":"Al-Hameed S, Benaissa M, Christensen H (2017) Detecting and predicting AD severity in longitudinal acoustic data. In: Proceedings of the international conference on bioinformatics research and applications; 2017. p. 57\u201361.","DOI":"10.1145\/3175587.3175589"},{"issue":"9859","key":"1853_CR7","doi-asserted-by":"publisher","first-page":"2095","DOI":"10.1016\/S0140-6736(12)61728-0","volume":"380","author":"R Lozano","year":"2012","unstructured":"Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, Remuzzi G. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380(9859):2095\u2013128.","journal-title":"Lancet"},{"issue":"1","key":"1853_CR8","first-page":"112","volume":"1","author":"A K\u00f6nig","year":"2015","unstructured":"K\u00f6nig A, Satt A, Sorin A, Hoory R, Toledo-Ronen O, Derreumaux A, David R. Automatic speech analysis for the assessment of patients with predementia and Alzheimer\u2019s disease. Alzheimer Dement Diagn Assess Dis Monit. 2015;1(1):112\u201324.","journal-title":"Alzheimer Dement Diagn Assess Dis Monit"},{"issue":"2","key":"1853_CR9","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.jalz.2013.02.003","volume":"9","author":"A Association","year":"2013","unstructured":"Association A, Thies W, Bleiler L. 2013 ADfacts and figures. Alzheimers Dement. 2013;9(2):208\u201345.","journal-title":"Alzheimers Dement"},{"key":"1853_CR10","doi-asserted-by":"crossref","unstructured":"Perdisci R, Giacinto G, Roli F. Alarm clustering for intrusion detection systems in computer networks. Eng Appl Artif Intell. 2006;19(4):429\u2013438.","DOI":"10.1016\/j.engappai.2006.01.003"},{"key":"1853_CR11","doi-asserted-by":"crossref","unstructured":"Satt A, Hoory R, K\u00f6nig A, Aalten P, Robert PH (2014) Speech-based automatic and robust detection of very early dementia. In: Fifteenth Annual Conference of the International Speech Communication Association.","DOI":"10.21437\/Interspeech.2014-544"},{"key":"1853_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102231","volume":"63","author":"S Kilicarslan","year":"2021","unstructured":"Kilicarslan S, Celik M, Sahin \u015e. Hybrid models based on genetic algorithm and DL algorithms for nutritional Anemia disease classification. Biomed Signal Process Control. 2021;63: 102231.","journal-title":"Biomed Signal Process Control"},{"issue":"2","key":"1853_CR13","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. Brain MRI analysis for ADdiagnosis using an ensemble system of deep convolutional neural networks. Brain Inform. 2018;5(2):1\u201314.","journal-title":"Brain Inform"},{"key":"1853_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2019.100200","volume":"16","author":"G Battineni","year":"2019","unstructured":"Battineni G, Chintalapudi N, Amenta F. ML in medicine: performance calculation of dementia prediction by support vector machines (SVM). Inform Med Unlocked. 2019;16:100200.","journal-title":"Inform Med Unlocked"},{"issue":"3","key":"1853_CR15","doi-asserted-by":"publisher","first-page":"3439","DOI":"10.1007\/s11042-021-11318-9","volume":"81","author":"J Tong","year":"2022","unstructured":"Tong J, Dou Q, Yang H, Jeon G, Yang X. Lightweight refined networks for single image super-resolution. Multimed Tools Appl. 2022;81(3):3439\u201358.","journal-title":"Multimed Tools Appl"},{"issue":"2","key":"1853_CR16","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1016\/j.bbe.2021.02.006","volume":"41","author":"X Zhao","year":"2021","unstructured":"Zhao X, Ang CKE, Acharya UR, Cheong KH. Application of Artificial Intelligence techniques for the detection of AD using structural MRI images. Biocybern Biomed Eng. 2021;41(2):456\u201373.","journal-title":"Biocybern Biomed Eng"},{"key":"1853_CR17","doi-asserted-by":"publisher","first-page":"220","DOI":"10.3389\/fnagi.2019.00220","volume":"11","author":"T Jo","year":"2019","unstructured":"Jo T, Nho K, Saykin AJ. DL in Alzheimer\u2019s disease: diagnostic classification and prognostic prediction using neuroimaging data. Front Aging Neurosci. 2019;11:220.","journal-title":"Front Aging Neurosci"},{"issue":"2","key":"1853_CR18","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.mehy.2004.09.005","volume":"64","author":"LL Cuddy","year":"2005","unstructured":"Cuddy LL, Duffin J. Music, memory, and Alzheimer\u2019s disease: is music recognition spared in dementia, and how can it be assessed? Med Hypotheses. 2005;64(2):229\u201335.","journal-title":"Med Hypotheses"},{"issue":"5","key":"1853_CR19","doi-asserted-by":"publisher","first-page":"1828","DOI":"10.1002\/hbm.22740","volume":"36","author":"J Wang","year":"2015","unstructured":"Wang J, Wang X, He Y, Yu X, Wang H, He Y. Apolipoprotein E \u03b54 modulates functional brain connectome in Alzheimer\u2019s disease. Hum Brain Mapp. 2015;36(5):1828\u201346.","journal-title":"Hum Brain Mapp"},{"issue":"19","key":"1853_CR20","doi-asserted-by":"publisher","first-page":"e2","DOI":"10.4108\/eai.13-7-2018.162806","volume":"5","author":"I Nissar","year":"2019","unstructured":"Nissar I, Rizvi DR, Masood S, Mir AN. Voice-based detection of Parkinson\u2019s disease through ensemble ML approach: a performance study. EAI Endorsed Trans Pervasive Health Technol. 2019;5(19):e2\u2013e2.","journal-title":"EAI Endorsed Trans Pervasive Health Technol"},{"issue":"2","key":"1853_CR21","doi-asserted-by":"publisher","first-page":"26","DOI":"10.15282\/ijsecs.7.2.2021.4.0087","volume":"7","author":"G Alqubati","year":"2021","unstructured":"Alqubati G, Algaphari G. ML and DL-based approaches on various biomarkers for AD early detection: a review. Int J Softw Eng Comput Syst. 2021;7(2):26\u201343.","journal-title":"Int J Softw Eng Comput Syst"},{"issue":"1","key":"1853_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12911-019-0974-x","volume":"19","author":"MJ Kang","year":"2019","unstructured":"Kang MJ, Kim SY, Na DL, Kim BC, Yang DW, Kim EJ, Youn YC. Prediction of cognitive impairment via DL trained with multi-center neuropsychological test data. BMC Med Inform Decis Mak. 2019;19(1):1\u20139.","journal-title":"BMC Med Inform Decis Mak"},{"key":"1853_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.105032","volume":"141","author":"A Loddo","year":"2022","unstructured":"Loddo A, Buttau S, Di Ruberto C. DL based pipelines for AD diagnosis: a comparative study and a novel deep-ensemble method. Comput Biol Med. 2022;141: 105032.","journal-title":"Comput Biol Med"},{"key":"1853_CR24","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.knosys.2017.10.017","volume":"139","author":"J Camps","year":"2018","unstructured":"Camps J, Sama A, Martin M, Rodriguez-Martin D, Perez-Lopez C, Arostegui JMM, Rodriguez-Molinero A. DL for freezing of gait detection in Parkinson\u2019s disease patients in their homes using a waist-worn inertial measurement unit. Knowl Based Syst. 2018;139:119\u201331.","journal-title":"Knowl Based Syst"},{"issue":"25","key":"1853_CR25","doi-asserted-by":"publisher","first-page":"10601","DOI":"10.1073\/pnas.0701096104","volume":"104","author":"N Zelcer","year":"2007","unstructured":"Zelcer N, Khanlou N, Clare R, Jiang Q, Reed-Geaghan EG, Landreth GE, Tontonoz P. Attenuation of neuroinflammation and AD pathology by liver x receptors. Proc Natl Acad Sci. 2007;104(25):10601\u20136.","journal-title":"Proc Natl Acad Sci"},{"issue":"1","key":"1853_CR26","first-page":"132","volume":"8","author":"MA Al-Hagery","year":"2020","unstructured":"Al-Hagery MA, Al-Fairouz EI, Al-Humaidan NA. Improvement of AD diagnosis accusing ensemble methods. Indones J Electr Eng Inform (IJEEI). 2020;8(1):132\u20139.","journal-title":"Indones J Electr Eng Inform (IJEEI)"},{"issue":"2","key":"1853_CR27","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1016\/j.pec.2009.06.005","volume":"78","author":"A Au","year":"2010","unstructured":"Au A, Li S, Lee K, Leung P, Pan PC, Thompson L, Gallagher-Thompson D. The coping with caregiving group program for Chinese caregivers of patients with AD in Hong Kong. Patient Educ Couns. 2010;78(2):256\u201360.","journal-title":"Patient Educ Couns"},{"issue":"4","key":"1853_CR28","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1007\/s10278-017-9983-4","volume":"30","author":"Z Akkus","year":"2017","unstructured":"Akkus Z, Galimzianova A, Hoogi A, Rubin DL, Erickson BJ. DL for brain MRI segmentation: state of the art and future directions. J Digit Imaging. 2017;30(4):449\u201359.","journal-title":"J Digit Imaging"},{"issue":"1","key":"1853_CR29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-37186-2","volume":"9","author":"G Lee","year":"2019","unstructured":"Lee G, Nho K, Kang B, Sohn KA, Kim D. Predicting AD progression using multi-modal DL approach. Sci Rep. 2019;9(1):1\u201312.","journal-title":"Sci Rep"},{"key":"1853_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2020.107009","volume":"150","author":"SJ Yang","year":"2020","unstructured":"Yang SJ, Shin H, Lee SH, et al. Functional linear regression model with randomly censored data: predicting conversion time to Alzheimer\u2019s disease. Comput Stat Data Anal. 2020;150: 107009.","journal-title":"Comput Stat Data Anal"},{"issue":"7","key":"1853_CR31","doi-asserted-by":"publisher","first-page":"1148","DOI":"10.1212\/01.WNL.0000118211.78503.F5","volume":"62","author":"JA Schneider","year":"2004","unstructured":"Schneider JA, Wilson RS, Bienias JL, et al. Cerebral infarctions and the likelihood of dementia from AD pathology. Neurology. 2004;62(7):1148\u201355.","journal-title":"Neurology"},{"key":"1853_CR32","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.patcog.2017.07.018","volume":"72","author":"P Cao","year":"2017","unstructured":"Cao P, Shan X, Zhao D, et al. Sparse shared structure based multi-task learning for MRI based cognitive performance prediction of Alzheimer\u2019s disease. Pattern Recogn. 2017;72:219\u201335.","journal-title":"Pattern Recogn"},{"key":"1853_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107247","volume":"102","author":"B Lei","year":"2020","unstructured":"Lei B, Yang M, Yang P, et al. Deep and joint learning of longitudinal data for ADprediction. Pattern Recogn. 2020;102: 107247.","journal-title":"Pattern Recogn"},{"key":"1853_CR34","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1016\/j.nicl.2015.05.006","volume":"8","author":"SJ Teipel","year":"2015","unstructured":"Teipel SJ, Kurth J, Krause B, et al. The relative importance of imaging markers for the prediction of AD dementia in mild cognitive impairment\u2014beyond classical regression. Neuroimage Clin. 2015;8:583\u201393.","journal-title":"Neuroimage Clin"},{"key":"1853_CR35","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1016\/j.nicl.2016.12.011","volume":"13","author":"E Moradi","year":"2017","unstructured":"Moradi E, Hallikainen I, H\u00e4nninen T, et al. Rey\u2019s auditory verbal learning test scores can be predicted from whole brain MRI in Alzheimer\u2019s disease. Neuroimage Clin. 2017;13:415\u201327.","journal-title":"Neuroimage Clin"},{"key":"1853_CR36","first-page":"755","volume":"10","author":"CC Luk","year":"2018","unstructured":"Luk CC, Ishaque A, Khan M, et al. Alzheimer\u2019s disease: 3-dimensional MRI texture for prediction of conversion from mild cognitive impairment. Alzheimers Dement Diagn Assess Dis Monit. 2018;10:755\u201363.","journal-title":"Alzheimers Dement Diagn Assess Dis Monit"},{"issue":"1","key":"1853_CR37","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1109\/TBME.2018.2824725","volume":"66","author":"T Zhou","year":"2018","unstructured":"Zhou T, Thung KH, Liu M, et al. Brain-wide genome-wide association study for AD via joint projection learning and sparse regression model. IEEE Trans Biomed Eng. 2018;66(1):165\u201375.","journal-title":"IEEE Trans Biomed Eng"},{"key":"1853_CR38","doi-asserted-by":"publisher","first-page":"76","DOI":"10.3389\/fnagi.2016.00076","volume":"8","author":"R Wei","year":"2016","unstructured":"Wei R, Li C, Fogelson N, et al. Prediction of conversion from mild cognitive impairment to ADusing MRI and structural network features. Front Aging Neurosci. 2016;8:76.","journal-title":"Front Aging Neurosci"},{"key":"1853_CR39","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.neurobiolaging.2016.07.005","volume":"46","author":"L Huang","year":"2016","unstructured":"Huang L, Jin Y, Gao Y, et al. Longitudinal clinical score prediction in AD with soft-split sparse regression based random forest. Neurobiol Aging. 2016;46:180\u201391.","journal-title":"Neurobiol Aging"},{"key":"1853_CR40","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/s10916-019-1475-2","volume":"44","author":"F Ramzan","year":"2019","unstructured":"Ramzan F, Khan MUG, Rehmat A, Iqbal S, Saba T, Rehman A, Mehmood Z. A DL approach for automated diagnosis and multi-class classification of AD stages using resting-state fMRI and residual neural networks. J Med Syst. 2019;44:37.","journal-title":"J Med Syst"},{"key":"1853_CR41","unstructured":"Classification and visualization of AD using volumetric convolutional neural network and transfer Learning|Scientific reports. https:\/\/www.nature.com\/articles\/s41598-019-54548-6. Accessed 13 Sept 2020."},{"issue":"3","key":"1853_CR42","first-page":"5005","volume":"70","author":"TM Ghazal","year":"2022","unstructured":"Ghazal TM, Issa G. Alzheimer disease detection empowered with transfer learning. Comput Mater Contin. 2022;70(3):5005\u201319.","journal-title":"Comput Mater Contin"},{"key":"1853_CR43","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.neuroscience.2021.01.002","volume":"460","author":"A Mehmood","year":"2021","unstructured":"Mehmood A, Yang S, Feng Z, Wang M, Ahmad AS, Khan R, Yaqub M. A transfer learning approach for early diagnosis of Alzheimer\u2019s disease on MRI images. Neuroscience. 2021;460:43\u201352.","journal-title":"Neuroscience"},{"issue":"1","key":"1853_CR44","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s00530-021-00797-3","volume":"28","author":"S Naz","year":"2022","unstructured":"Naz S, Ashraf A, Zaib A. Transfer learning using freeze features for Alzheimer neurological disorder detection using ADNI dataset. Multimed Syst. 2022;28(1):85\u201394.","journal-title":"Multimed Syst"},{"key":"1853_CR45","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11042-020-10331-8","volume":"80","author":"A Ashraf","year":"2021","unstructured":"Ashraf A, Naz S, Shirazi SH, Razzak I, Parsad M. Deep transfer learning for alzheimer neurological disorder detection. Multimed Tools Appl. 2021;80:1\u201326.","journal-title":"Multimed Tools Appl"},{"issue":"4","key":"1853_CR46","doi-asserted-by":"publisher","first-page":"1453","DOI":"10.1109\/JBHI.2021.3083274","volume":"26","author":"M Tanveer","year":"2021","unstructured":"Tanveer M, Rashid AH, Ganaie MA, Reza M, Razzak I, Hua KL. Classification of Alzheimer\u2019s disease using ensemble of deep neural networks trained through transfer learning. IEEE J Biomed Health Inform. 2021;26(4):1453\u201363.","journal-title":"IEEE J Biomed Health Inform"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-023-01853-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-023-01853-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-023-01853-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,17]],"date-time":"2023-06-17T15:24:18Z","timestamp":1687015458000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-023-01853-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,18]]},"references-count":46,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["1853"],"URL":"https:\/\/doi.org\/10.1007\/s42979-023-01853-7","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,18]]},"assertion":[{"value":"28 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 April 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 May 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No conflict of interest is disclosed.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}],"article-number":"404"}}