{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T23:26:02Z","timestamp":1778541962104,"version":"3.51.4"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,10,2]],"date-time":"2022-10-02T00:00:00Z","timestamp":1664668800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,10,2]],"date-time":"2022-10-02T00:00:00Z","timestamp":1664668800000},"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":["Evolving Systems"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s12530-022-09467-9","type":"journal-article","created":{"date-parts":[[2022,10,2]],"date-time":"2022-10-02T16:02:41Z","timestamp":1664726561000},"page":"801-824","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Multi-class classification of Alzheimer\u2019s disease through distinct neuroimaging computational approaches using Florbetapir PET scans"],"prefix":"10.1007","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4886-1830","authenticated-orcid":false,"given":"Nitika","family":"Goenka","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shamik","family":"Tiwari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,2]]},"reference":[{"key":"9467_CR1","first-page":"16","volume":"I","author":"M Abadi","year":"2016","unstructured":"Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, Kudlur M, Levenberg J, Monga R, Moore S, Murray DG, Steiner B, Tucker P, Vasudevan V, Warden P et al (2016) TensorFlow: a system for large-scale machine learning. OSD I:16","journal-title":"OSD"},{"key":"9467_CR2","unstructured":"ADNI Dataset (2022) http:\/\/adni.loni.usc.edu\/"},{"key":"9467_CR3","unstructured":"Alzheimer\u2019s Disease International (2022) https:\/\/www.alzint.org\/about\/dementia-facts-figures\/dementia-statistics\/"},{"key":"9467_CR4","unstructured":"ANTs (2022) http:\/\/stnava.github.io\/ANTs\/"},{"issue":"4","key":"9467_CR5","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1007\/s00259-011-2021-8","volume":"39","author":"V Camus","year":"2012","unstructured":"Camus V, Payoux P, Barr\u00e9 L, Desgranges B, Voisin T, Tauber C, La Joie R, Tafani M, Hommet C, Ch\u00e9telat G, Mondon K, De La Sayette V, Cottier JP, Beaufils E, Ribeiro MJ, Gissot V, Vierron E, Vercouillie J, Vellas B et al (2012) Using PET with 18F-AV-45 (florbetapir) to quantify brain amyloid load in a clinical environment. Eur J Nucl Med Mol Imaging 39(4):621\u2013631. https:\/\/doi.org\/10.1007\/s00259-011-2021-8","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"9467_CR6","doi-asserted-by":"crossref","unstructured":"Choi H, Jin KH (2018) Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging. Behav Brain Res 344:103\u2013109. http:\/\/adni.loni.usc.edu\/wp-content\/uploads\/how_to_apply\/ADNI_Acknowledgement_List.pdf","DOI":"10.1016\/j.bbr.2018.02.017"},{"key":"9467_CR7","unstructured":"Chollet F (2015) Keras"},{"key":"9467_CR8","unstructured":"DARTEL toolbox (2022) https:\/\/neurometrika.org\/node\/34"},{"key":"9467_CR9","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.neucom.2020.05.087","volume":"412","author":"S El-sappagh","year":"2020","unstructured":"El-sappagh S, Abuhmed T, Islam SMR, Sup K (2020) Multimodal multitask deep learning model for Alzheimer\u2019s disease progression detection based on time series data. Neurocomputing 412:197\u2013215","journal-title":"Neurocomputing"},{"key":"9467_CR10","unstructured":"FLIRT (2022) https:\/\/fsl.fmrib.ox.ac.uk\/fsl\/fslwiki\/FLIRT"},{"issue":"1","key":"9467_CR11","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/j.neuroimage.2010.07.033","volume":"54","author":"V Fonov","year":"2011","unstructured":"Fonov V, Evans AC, Botteron K, Almli CR, Mckinstry RC, Collins DL (2011) Unbiased average age-appropriate atlases for pediatric studies. Neuroimage 54(1):313\u2013327","journal-title":"Neuroimage"},{"key":"9467_CR12","unstructured":"FreeSurfer (2022) https:\/\/surfer.nmr.mgh.harvard.edu\/"},{"key":"9467_CR13","unstructured":"FSL (2022) https:\/\/fsl.fmrib.ox.ac.uk\/fsl\/fslwiki"},{"key":"9467_CR18","unstructured":"Goenka N, Sharma DK (2020) Carebot: a cognitive behavioural therapy agent using deep learning for COVID-19. 7(19):6100\u20136108"},{"issue":"7","key":"9467_CR14","doi-asserted-by":"publisher","first-page":"4827","DOI":"10.1007\/s10462-021-10016-0","volume":"54","author":"N Goenka","year":"2021","unstructured":"Goenka N, Tiwari S (2021a) Deep learning for Alzheimer prediction using brain biomarkers. Artif Intell Rev 54(7):4827\u20134871","journal-title":"Artif Intell Rev"},{"key":"9467_CR19","doi-asserted-by":"crossref","unstructured":"Goenka N, Tiwari S (2021b) Volumetric convolutional neural network for alzheimer detection. ICOEI 1500\u20131505","DOI":"10.1109\/ICOEI51242.2021.9453043"},{"issue":"September 2021","key":"9467_CR15","doi-asserted-by":"publisher","first-page":"103500","DOI":"10.1016\/j.bspc.2022.103500","volume":"74","author":"N Goenka","year":"2022","unstructured":"Goenka N, Tiwari S (2022) AlzVNet: A volumetric convolutional neural network for multiclass classification of Alzheimer\u2019s disease through multiple neuroimaging computational approaches. Biomed Signal Process Control 74(September 2021):103500. https:\/\/doi.org\/10.1016\/j.bspc.2022.103500","journal-title":"Biomed Signal Process Control"},{"issue":"3","key":"9467_CR16","doi-asserted-by":"publisher","first-page":"1175","DOI":"10.37418\/amsj.10.3.7","volume":"10","author":"N Goenka","year":"2021","unstructured":"Goenka N, Tiwari S, Yadav D (2021) No-reference image blur detection scheme using fuzzy inference. Adv Math Sci J 10(3):1175\u20131182","journal-title":"Adv Math Sci J"},{"key":"9467_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ESCI53509.2022.9758317","volume":"2022","author":"N Goenka","year":"2022","unstructured":"Goenka N, Goenka A, Tiwari S (2022) Patch-based classification for Alzheimer disease using sMRI. Int Conf Emerg Smart Comput Inform (ESCI) 2022:1\u20135. https:\/\/doi.org\/10.1109\/ESCI53509.2022.9758317","journal-title":"Int Conf Emerg Smart Comput Inform (ESCI)"},{"key":"9467_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmrp.2019.11.005","author":"A Haleem","year":"2019","unstructured":"Haleem A, Javaid M, Khan IH, Tech B, Engineering C (2019) Current status and applications of Artificial Intelligence (AI) in medical field: an overview. CMRP. https:\/\/doi.org\/10.1016\/j.cmrp.2019.11.005","journal-title":"CMRP"},{"key":"9467_CR012","doi-asserted-by":"publisher","unstructured":"Hao X, Bao Y, Guo Y, Ming Y,  Zhang, Daoqiang, Risacher S, Saykin A, Yao Xiaohui, Shen L (2019) Multi-modal neuroimaging feature selection with consistent metric constraint for diagnosis of Alzheimer\u2019s disease. Med Image Anal 60:101625. https:\/\/doi.org\/10.1016\/j.media.2019.101625","DOI":"10.1016\/j.media.2019.101625"},{"issue":"5","key":"9467_CR21","first-page":"584","volume":"23","author":"E Hosseini-Asl","year":"2018","unstructured":"Hosseini-Asl E, Ghazal M, Mahmoud A, Aslantas A, Shalaby A, Barnes G, Gimel G, Keynton R, Baz AE (2018) Alzheimer\u2019s disease diagnostics by a 3D deeply supervised adaptable convolutional network. Front Biosci 23(5):584\u2013596","journal-title":"Front Biosci"},{"issue":"May","key":"9467_CR22","doi-asserted-by":"publisher","first-page":"509","DOI":"10.3389\/fnins.2019.00509","volume":"13","author":"Y Huang","year":"2019","unstructured":"Huang Y, Xu J, Zhou Y, Tong T, Zhuang X (2019) Diagnosis of Alzheimer\u2019s disease via multi-modality 3D convolutional neural network. Front Neurosci 13(May):509","journal-title":"Front Neurosci"},{"key":"9467_CR23","first-page":"1","volume":"1","author":"RR Janghel","year":"2020","unstructured":"Janghel RR, Rathore YK (2020) Deep convolution neural network based system for early diagnosis of Alzheimer\u2019s disease. IRBM 1:1\u201310","journal-title":"IRBM"},{"key":"9467_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.jalz.2012.10.007","author":"KA Johnson","year":"2013","unstructured":"Johnson KA, Sperling RA, Gidicsin CM, Carmasin JS, Maye JE, Coleman RE, Reiman EM, Sabbagh MN, Sadowsky CH, Fleisher AS, Murali Doraiswamy P, Carpenter AP, Clark CM, Joshi AD, Lu M, Grundman M, Mintun MA, Pontecorvo MJ, Skovronsky DM (2013) Florbetapir (F18-AV-45) PET to assess amyloid burden in Alzheimer\u2019s disease dementia, mild cognitive impairment, and normal aging. Alzheimer\u2019s Dementia. https:\/\/doi.org\/10.1016\/j.jalz.2012.10.007","journal-title":"Alzheimer\u2019s Dementia"},{"issue":"February","key":"9467_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.117890","volume":"232","author":"SK Kang","year":"2021","unstructured":"Kang SK, Choi H, Lee JS (2021) Translating amyloid PET of different radiotracers by a deep generative model for interchangeability. Neuroimage 232(February):117890. https:\/\/doi.org\/10.1016\/j.neuroimage.2021.117890","journal-title":"Neuroimage"},{"key":"9467_CR26","doi-asserted-by":"crossref","unstructured":"Khan T (2016a) Alzheimer\u2019 s disease cerebrospinal fluid (CSF) biomarkers. In Biomarkers in Alzheimer\u2019s Disease, pp 139\u2013180","DOI":"10.1016\/B978-0-12-804832-0.00005-5"},{"key":"9467_CR27","doi-asserted-by":"crossref","unstructured":"Khan T (2016b) Genetic biomarkers in Alzheimer\u2019s disease. In Khan TK (ed) Biomarkers in Alzheimer\u2019s disease, vol 1. Academic Press, pp. 103\u2013135","DOI":"10.1016\/B978-0-12-804832-0.00004-3"},{"key":"9467_CR28","unstructured":"Kingma DP, Ba JL (2015) Adam: a method for stochastic optimization. In: 3rd international conference on learning representations, ICLR 2015\u2014conference track proceedings, pp 1\u201315."},{"issue":"November 2018","key":"9467_CR29","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.imu.2018.12.003","volume":"14","author":"KR Kruthika","year":"2019","unstructured":"Kruthika KR, Rajeswari, & Mahesappa, H. D. (2019) Multistage classifier-based approach for Alzheimer\u2019 s disease prediction and retrieval. Inform Med Unlocked 14(November 2018):34\u201342","journal-title":"Inform Med Unlocked"},{"issue":"1","key":"9467_CR30","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 K, Kim D (2019) Predicting Alzheimer\u2019s disease progression using multi-modal deep learning approach. Sci Rep 9(1):1\u201312","journal-title":"Sci Rep"},{"key":"9467_CR31","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.neucom.2020.01.053","volume":"388","author":"W Li","year":"2020","unstructured":"Li W, Lin X, Chen X (2020) Detecting Alzheimer\u2019s disease based on 4D fMRI: an exploration under deep learning framework. Neurocomputing 388:280\u2013287","journal-title":"Neurocomputing"},{"key":"9467_CR32","doi-asserted-by":"crossref","unstructured":"Lin M, Chen Q, Yan S (2014) Network in network. ArXiv, pp 1\u201310","DOI":"10.1155\/2014\/594350"},{"key":"9467_CR032","doi-asserted-by":"publisher","unstructured":"Liu M, Cheng D, Wang K, Wang Y, Alzheimer\u2019s Disease Neuroimaging Initiative (2018) Multi-modality cascaded convolutional neural Networks for Alzheimer's disease diagnosis. Neuroinform 16(3-4):295\u2013308. https:\/\/doi.org\/10.1007\/s12021-018-9370-4","DOI":"10.1007\/s12021-018-9370-4"},{"issue":"4","key":"9467_CR33","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 (2015) Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer\u2019s disease. IEEE Trans Biomed Eng 62(4):1132\u20131140","journal-title":"IEEE Trans Biomed Eng"},{"key":"9467_CR34","first-page":"1","volume":"5","author":"F M\u00e1rquez","year":"2019","unstructured":"M\u00e1rquez F, Yassa MA (2019) Neuroimaging biomarkers for Alzheimer\u2019s disease. Mol Neurodegener 5:1\u201314","journal-title":"Mol Neurodegener"},{"key":"9467_CR35","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1007\/s00259-015-3228-x","volume":"43","author":"E Morris","year":"2016","unstructured":"Morris E, Chalkidou A, Hammers A, Peacock J, Summers J, Keevil S (2016) Diagnostic accuracy of 18 F amyloid PET tracers for the diagnosis of Alzheimer\u2019s disease: a systematic review and meta-analysis. Eur J Nucl Med Mol Imaging 43:374\u2013385. https:\/\/doi.org\/10.1007\/s00259-015-3228-x","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"9467_CR36","unstructured":"Muschelli J (2022a) Brain Extraction\/Segmentation"},{"key":"9467_CR37","doi-asserted-by":"publisher","unstructured":"Muschelli J (2022b) Image Registration. https:\/\/doi.org\/10.1007\/978-3-642-41714-6_90345","DOI":"10.1007\/978-3-642-41714-6_90345"},{"issue":"12","key":"9467_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0226577","volume":"14","author":"I Ozsahin","year":"2019","unstructured":"Ozsahin I, Sekeroglu B, Mok GSP (2019) The use of back propagation neural networks and 18F-Florbetapir PET for early detection of Alzheimer\u2019s disease using Alzheimer\u2019s Disease Neuroimaging Initiative database. PLoS ONE 14(12):1\u201313. https:\/\/doi.org\/10.1371\/journal.pone.0226577","journal-title":"PLoS ONE"},{"key":"9467_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104919","volume":"138","author":"Z Peng","year":"2021","unstructured":"Peng Z, Ni M, Shan H, Lu Y, Li Y, Zhang Y, Pei X, Chen Z, Xie Q, Wang S, Xu XG (2021) Feasibility evaluation of PET scan-time reduction for diagnosing amyloid-\u03b2 levels in Alzheimer\u2019s disease patients using a deep-learning-based denoising algorithm. Comput Biol Med 138:104919. https:\/\/doi.org\/10.1016\/j.compbiomed.2021.104919","journal-title":"Comput Biol Med"},{"key":"9467_CR40","doi-asserted-by":"publisher","DOI":"10.1097\/WCO.0000000000000109","author":"D Perani","year":"2014","unstructured":"Perani D (2014) FDG-PET and amyloid-PET imaging: the diverging paths. Curr Opin Neurol. https:\/\/doi.org\/10.1097\/WCO.0000000000000109","journal-title":"Curr Opin Neurol"},{"key":"9467_CR41","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0225759","author":"A Punjabi","year":"2019","unstructured":"Punjabi A, Martersteck A, Wang Y, Parrish TB, Katsaggelos AK (2019) Neuroimaging modality fusion in Alzheimer\u2019s classification using convolutional neural networks. PLoS ONE. https:\/\/doi.org\/10.1371\/journal.pone.0225759","journal-title":"PLoS ONE"},{"issue":"12","key":"9467_CR42","doi-asserted-by":"publisher","first-page":"7113","DOI":"10.1007\/s13369-017-2952-x","volume":"43","author":"Y Qin","year":"2018","unstructured":"Qin Y, Tian C (2018) Weighted feature space representation with Kernel for image classification. Arab J Sci Eng 43(12):7113\u20137125. https:\/\/doi.org\/10.1007\/s13369-017-2952-x","journal-title":"Arab J Sci Eng"},{"key":"9467_CR43","first-page":"737","volume":"10","author":"S Qiu","year":"2018","unstructured":"Qiu S, Chang GH, Panagia M, Gopal DM, Au R (2018) Fusion of deep learning models of MRI scans, Mini-Mental State Examination, and logical memory test enhances diagnosis of mild cognitive impairment. Alzheimer\u2019s Dementia Diagn Assess Dis Monitor 10:737\u2013749","journal-title":"Alzheimer\u2019s Dementia Diagn Assess Dis Monitor"},{"issue":"2","key":"9467_CR44","doi-asserted-by":"publisher","first-page":"485","DOI":"10.3390\/app10020485","volume":"10","author":"L Qu","year":"2020","unstructured":"Qu L, Wu C, Zou L (2020) 3D Dense separated convolution module for volumetric medical image analysis. Appl Sci 10(2):485","journal-title":"Appl Sci"},{"key":"9467_CR45","doi-asserted-by":"crossref","unstructured":"Reith F, Koran ME, Davidzon G, Zaharchuk G (2020) Application of deep learning to predict standardized uptake value ratio and amyloid status on 18 F-florbetapir. Am J Neuroradiol 1\u20137","DOI":"10.3174\/ajnr.A6573"},{"key":"9467_CR46","doi-asserted-by":"crossref","unstructured":"Sahumbaiev I, Popov A, Ivanushkina N, Ram\u00edrez J, G\u00f3rriz JM (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":"9467_CR47","doi-asserted-by":"publisher","DOI":"10.1109\/access.2022.3149824","author":"AK Sharma","year":"2022","unstructured":"Sharma AK, Tiwari S, Aggarwal G, Goenka N, Kumar A, Chakrabarti P, Chakrabarti T, Gono R, Leonowicz Z, Jasinski M (2022) Dermatologist-level classification of skin cancer using cascaded ensembling of convolutional neural network and handcrafted features based deep neural network. IEEE Access. https:\/\/doi.org\/10.1109\/access.2022.3149824","journal-title":"IEEE Access"},{"key":"9467_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2021.100710","volume":"25","author":"K Shirbandi","year":"2021","unstructured":"Shirbandi K, Khalafi M, Mirza-Aghazadeh-Attari M, Tahmasbi M, Kiani Shahvandi H, Javanmardi P, Rahim F (2021) Accuracy of deep learning model-assisted amyloid positron emission tomography scan in predicting Alzheimer\u2019s disease: a systematic review and meta-analysis. Inform Med Unlocked 25:100710. https:\/\/doi.org\/10.1016\/j.imu.2021.100710","journal-title":"Inform Med Unlocked"},{"issue":"3","key":"9467_CR49","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1002\/hbm.10062","volume":"17","author":"SM Smith","year":"2002","unstructured":"Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17(3):143\u2013155","journal-title":"Hum Brain Mapp"},{"key":"9467_CR50","unstructured":"SPM (2022) https:\/\/www.fil.ion.ucl.ac.uk\/spm\/"},{"key":"9467_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2014.06.077","author":"H Suk","year":"2014","unstructured":"Suk H, Lee S, Shen D, Initiative N (2014) Hierarchical feature representation and multimodal fusion with deep learning for AD \/ MCI diagnosis. Neuroimage. https:\/\/doi.org\/10.1016\/j.neuroimage.2014.06.077","journal-title":"Neuroimage"},{"key":"9467_CR52","doi-asserted-by":"crossref","unstructured":"Wang Y, Yang Y, Guo X, Ye C, Gao N, Fang Y, Ma HT, Ieee M (2018) A novel multimodal MRI analysis for Alzheimer\u2019s disease based on convolutional neural network. EMBC 754\u2013757","DOI":"10.1109\/EMBC.2018.8512372"},{"key":"9467_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101694","volume":"63","author":"J Wen","year":"2020","unstructured":"Wen J, Thibeau-sutre E, Diaz-melo M (2020) Convolutional neural networks for classification of Alzheimer\u2019s disease: overview and reproducible evaluation. Med Image Anal 63:101694","journal-title":"Med Image Anal"},{"issue":"3","key":"9467_CR54","doi-asserted-by":"publisher","first-page":"1742","DOI":"10.3390\/app12031742","volume":"12","author":"J Xiao","year":"2022","unstructured":"Xiao J, Xu J, Tian C, Han P, You L, Zhang S (2022) A Serial attention frame for multi-label waste bottle classification. Appl Sci 12(3):1742. https:\/\/doi.org\/10.3390\/app12031742","journal-title":"Appl Sci"},{"issue":"3","key":"9467_CR55","doi-asserted-by":"publisher","first-page":"1311","DOI":"10.37418\/amsj.10.3.18","volume":"10","author":"D Yadav","year":"2021","unstructured":"Yadav D, Goenka N (2021) Comparative analysis of newton raphson and particle swarm optimization techniques for harmonic minimization in CMLI. Adv Math Sci J 10(3):1311\u20131317. https:\/\/doi.org\/10.37418\/amsj.10.3.18","journal-title":"Adv Math Sci J"},{"key":"9467_CR56","doi-asserted-by":"publisher","first-page":"P315","DOI":"10.1016\/j.jalz.2018.06.121","volume":"14","author":"Y Yuan","year":"2018","unstructured":"Yuan Y, Wang Z, Lee W, VanGilder P, Chen Y, Reiman EM, Chen K (2018) Quantification of amyloid burden from florbetapir pet without using target and reference regions: preliminary findings based on the deep learning 3D convolutional neural network approach. Alzheimer\u2019s Dementia 14:P315\u2013P316. https:\/\/doi.org\/10.1016\/j.jalz.2018.06.121","journal-title":"Alzheimer\u2019s Dementia"},{"key":"9467_CR57","doi-asserted-by":"publisher","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","journal-title":"J Neurosci Methods"},{"issue":"1","key":"9467_CR58","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/2171\/1\/012068","volume":"2171","author":"M Zheng","year":"2022","unstructured":"Zheng M, Xu J, Shen Y, Tian C, Li J (2022) Attention-based CNNs for image classification: a survey. J Phys Conf Ser 2171(1):012068. https:\/\/doi.org\/10.1088\/1742-6596\/2171\/1\/012068","journal-title":"J Phys Conf Ser"},{"key":"9467_CR59","unstructured":"Zunair H, Rahman A, Mohammed N (2019) Estimating severity from CT scans of tuberculosis patients using 3D convolutional nets and slice selection. CLEF 9\u201312"},{"key":"9467_CR60","doi-asserted-by":"crossref","unstructured":"Zunair H, Rahman A, Mohammed N, Cohen JP (2020) Uniformizing techniques to process CT scans with 3D CNNs for tuberculosis prediction. ArXiv pp 1\u201312","DOI":"10.1007\/978-3-030-59354-4_15"}],"container-title":["Evolving Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12530-022-09467-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12530-022-09467-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12530-022-09467-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T04:17:09Z","timestamp":1695874629000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12530-022-09467-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,2]]},"references-count":62,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["9467"],"URL":"https:\/\/doi.org\/10.1007\/s12530-022-09467-9","relation":{},"ISSN":["1868-6478","1868-6486"],"issn-type":[{"value":"1868-6478","type":"print"},{"value":"1868-6486","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,2]]},"assertion":[{"value":"15 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 October 2022","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 conflicts of interest declared.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interests"}}]}}