{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,11]],"date-time":"2026-11-11T21:13:53Z","timestamp":1794431633182,"version":"4.9.1"},"reference-count":130,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T00:00:00Z","timestamp":1742515200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T00:00:00Z","timestamp":1742515200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R35GM153434"],"award-info":[{"award-number":["R35GM153434"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000143","name":"Division of Computing and Communication Foundations","doi-asserted-by":"publisher","award":["OAC-2312599"],"award-info":[{"award-number":["OAC-2312599"]}],"id":[{"id":"10.13039\/100000143","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Brain Inf."],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Clinical diagnosis of Alzheimer\u2019s disease (AD) is usually made after symptoms such as short-term memory loss are exhibited, which minimizes the intervention and treatment options. The existing screening techniques cannot distinguish between stable MCI (sMCI) cases (i.e., patients who do not convert to AD for at least three years) and progressive MCI (pMCI) cases (i.e., patients who convert to AD in three years or sooner). Delayed diagnosis of AD also disproportionately affects underrepresented and socioeconomically disadvantaged populations. The significant positive impact of an early diagnosis solution for AD across diverse ethno-racial and demographic groups is well-known and recognized. While advancements in high-throughput technologies have enabled the generation of vast amounts of multimodal clinical, and neuroimaging datasets related to AD, most methods utilizing these data sets for diagnostic purposes have not found their way in clinical settings. To better understand the landscape, we surveyed the major preprocessing, data management, traditional machine-learning (ML), and deep learning (DL) techniques used for diagnosing AD using neuroimaging data such as structural magnetic resonance imaging (sMRI), functional magnetic resonance imaging (fMRI), and positron emission tomography (PET). Once we had a good understanding of the methods available, we conducted a study to assess the reproducibility and generalizability of open-source ML models. Our evaluation shows that existing models show reduced generalizability when different cohorts of the data modality are used while controlling other computational factors. The paper concludes with a discussion of major challenges that plague ML models for AD diagnosis and biomarker discovery.<\/jats:p>","DOI":"10.1186\/s40708-025-00252-3","type":"journal-article","created":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T15:54:57Z","timestamp":1742572497000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":48,"title":["Machine-learning models for Alzheimer\u2019s disease diagnosis using neuroimaging data: survey, reproducibility, and generalizability evaluation"],"prefix":"10.1186","volume":"12","author":[{"given":"Maryam Akhavan","family":"Aghdam","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Serdar","family":"Bozdag","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fahad","family":"Saeed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,21]]},"reference":[{"issue":"11","key":"252_CR1","doi-asserted-by":"publisher","first-page":"1579","DOI":"10.1586\/ern.11.155","volume":"11","author":"S Karantzoulis","year":"2011","unstructured":"Karantzoulis S, Galvin JE (2011) Distinguishing Alzheimer\u2019s disease from other major forms of dementia. Expert Rev Neurother 11(11):1579\u20131591. https:\/\/doi.org\/10.1586\/ern.11.155","journal-title":"Expert Rev Neurother"},{"key":"252_CR2","unstructured":"What is Alzheimer\u2019s Disease? | CDC. https:\/\/www.cdc.gov\/aging\/aginginfo\/alzheimers.htm. Accessed 29 Dec 2023"},{"issue":"1","key":"252_CR3","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1373\/jalm.2019.029587","volume":"5","author":"LM Shaw","year":"2020","unstructured":"Shaw LM et al (2020) Detection of Alzheimer disease pathology in patients using biochemical biomarkers: prospects and challenges for use in clinical practice. J Appl Lab Med 5(1):183\u2013193. https:\/\/doi.org\/10.1373\/jalm.2019.029587","journal-title":"J Appl Lab Med"},{"issue":"12","key":"252_CR4","doi-asserted-by":"publisher","first-page":"1217","DOI":"10.1002\/gps.1816","volume":"22","author":"L Rozzini","year":"2007","unstructured":"Rozzini L et al (2007) Conversion of amnestic mild cognitive impairment to dementia of Alzheimer type is independent to memory deterioration. Int J Geriatr Psychiatry 22(12):1217\u20131222. https:\/\/doi.org\/10.1002\/gps.1816","journal-title":"Int J Geriatr Psychiatry"},{"key":"252_CR5","unstructured":"New Alzheimer\u2019s Association Report, Alzheimer\u2019s Association. https:\/\/www.alz.org\/news\/2022\/facts-figures-alzheimers-mild-cognitive-impairment. Accessed 29 Dec 2023"},{"key":"252_CR6","unstructured":"Health and economic costs of chronic diseases | CDC. https:\/\/www.cdc.gov\/chronicdisease\/about\/costs\/index.htm. Accessed 29 Dec 2023"},{"issue":"6","key":"252_CR7","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1007\/s10278-018-0093-8","volume":"31","author":"M Akhavan Aghdam","year":"2018","unstructured":"Akhavan Aghdam M, Sharifi A, Pedram MM (2018) Combination of rs-fMRI and sMRI data to discriminate autism spectrum disorders in young children using deep belief network. J Digit Imaging 31(6):895\u2013903. https:\/\/doi.org\/10.1007\/s10278-018-0093-8","journal-title":"J Digit Imaging"},{"key":"252_CR8","doi-asserted-by":"publisher","first-page":"70","DOI":"10.3389\/fninf.2019.00070","volume":"13","author":"T Eslami","year":"2019","unstructured":"Eslami T, Mirjalili V, Fong A, Laird AR, Saeed F (2019) ASD-DiagNet: a hybrid learning approach for detection of autism spectrum disorder using fMRI data. Front Neuroinform 13:70. https:\/\/doi.org\/10.3389\/fninf.2019.00070","journal-title":"Front Neuroinform"},{"issue":"8","key":"252_CR9","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1515\/revneuro-2020-0043","volume":"31","author":"HS Nogay","year":"2020","unstructured":"Nogay HS, Adeli H (2020) Machine learning (ML) for the diagnosis of autism spectrum disorder (ASD) using brain imaging. Rev Neurosci 31(8):825\u2013841. https:\/\/doi.org\/10.1515\/revneuro-2020-0043","journal-title":"Rev Neurosci"},{"key":"252_CR10","doi-asserted-by":"publisher","DOI":"10.3389\/fncom.2021.654315","volume":"15","author":"F Almuqhim","year":"2021","unstructured":"Almuqhim F, Saeed F (2021) ASD-SAENet: a sparse autoencoder, and deep-neural network model for detecting autism spectrum disorder (ASD) using fMRI data. Front Comput Neurosci 15:654315. https:\/\/doi.org\/10.3389\/fncom.2021.654315","journal-title":"Front Comput Neurosci"},{"key":"252_CR11","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.neucom.2018.09.025","volume":"320","author":"B Gong","year":"2018","unstructured":"Gong B et al (2018) Neuroimaging-based diagnosis of Parkinson\u2019s disease with deep neural mapping large margin distribution machine. Neurocomputing 320:141\u2013149. https:\/\/doi.org\/10.1016\/j.neucom.2018.09.025","journal-title":"Neurocomputing"},{"issue":"11","key":"252_CR12","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/s10916-015-0353-9","volume":"39","author":"TJ Hirschauer","year":"2015","unstructured":"Hirschauer TJ, Adeli H, Buford JA (2015) Computer-aided diagnosis of Parkinson\u2019s disease using enhanced probabilistic neural network. J Med Syst 39(11):179. https:\/\/doi.org\/10.1007\/s10916-015-0353-9","journal-title":"J Med Syst"},{"issue":"1101","key":"252_CR13","doi-asserted-by":"publisher","first-page":"20180886","DOI":"10.1259\/bjr.20180886","volume":"92","author":"C Rubbert","year":"2019","unstructured":"Rubbert C et al (2019) Machine-learning identifies Parkinson\u2019s disease patients based on resting-state between-network functional connectivity. Br J Radiol 92(1101):20180886. https:\/\/doi.org\/10.1259\/bjr.20180886","journal-title":"Br J Radiol"},{"key":"252_CR14","doi-asserted-by":"publisher","unstructured":"Aderghal K, Khvostikov A, Krylov A, Benois-Pineau J, Afdel K, Catheline G (2018) Classification of Alzheimer disease on imaging modalities with deep CNNs using cross-modal transfer learning. In 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), Karlstad: IEEE, pp. 345\u2013350. https:\/\/doi.org\/10.1109\/CBMS.2018.00067","DOI":"10.1109\/CBMS.2018.00067"},{"key":"252_CR15","doi-asserted-by":"publisher","unstructured":"Backstrom K, Nazari M, Gu IY-H, 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), Washington, DC: IEEE, pp. 149\u2013153. https:\/\/doi.org\/10.1109\/ISBI.2018.8363543","DOI":"10.1109\/ISBI.2018.8363543"},{"key":"252_CR16","doi-asserted-by":"publisher","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), Beijing: IEEE, pp. 1\u20136. https:\/\/doi.org\/10.1109\/IST.2017.8261460","DOI":"10.1109\/IST.2017.8261460"},{"key":"252_CR17","doi-asserted-by":"publisher","first-page":"155584","DOI":"10.1109\/ACCESS.2019.2949577","volume":"7","author":"S Sarraf","year":"2019","unstructured":"Sarraf S, Desouza DD, Anderson JAE, Saverino C (2019) MCADNNet: recognizing stages of cognitive impairment through efficient convolutional fMRI and MRI neural network topology models. IEEE Access 7:155584\u2013155600. https:\/\/doi.org\/10.1109\/ACCESS.2019.2949577","journal-title":"IEEE Access"},{"issue":"2","key":"252_CR18","doi-asserted-by":"publisher","first-page":"260","DOI":"10.3390\/brainsci13020260","volume":"13","author":"S Sarraf","year":"2023","unstructured":"Sarraf S, Sarraf A, DeSouza DD, Anderson JAE, Kabia M, The Alzheimer\u2019s Disease Neuroimaging Initiative (2023) OViTAD: optimized vision transformer to predict various stages of Alzheimer\u2019s disease using resting-state fMRI and structural MRI data. Brain Sci 13(2):260. https:\/\/doi.org\/10.3390\/brainsci13020260","journal-title":"Brain Sci"},{"key":"252_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ijcce.2021.12.002","volume":"3","author":"S Gao","year":"2022","unstructured":"Gao S, Lima D (2022) A review of the application of deep learning in the detection of Alzheimer\u2019s disease. Int J Cogn Comput Eng 3:1\u20138. https:\/\/doi.org\/10.1016\/j.ijcce.2021.12.002","journal-title":"Int J Cogn Comput Eng"},{"key":"252_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2022.102332","volume":"130","author":"M Khojaste-Sarakhsi","year":"2022","unstructured":"Khojaste-Sarakhsi M, Haghighi SS, Ghomi SMTF, Marchiori E (2022) Deep learning for Alzheimer\u2019s disease diagnosis: a survey. Artif Intell Med 130:102332. https:\/\/doi.org\/10.1016\/j.artmed.2022.102332","journal-title":"Artif Intell Med"},{"key":"252_CR21","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.jchemneu.2018.02.006","volume":"95","author":"RN Annavarapu","year":"2019","unstructured":"Annavarapu RN, Kathi S, Vadla VK (2019) Non-invasive imaging modalities to study neurodegenerative diseases of aging brain. J Chem Neuroanat 95:54\u201369. https:\/\/doi.org\/10.1016\/j.jchemneu.2018.02.006","journal-title":"J Chem Neuroanat"},{"key":"252_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2019.105242","volume":"187","author":"MA Ebrahimighahnavieh","year":"2020","unstructured":"Ebrahimighahnavieh MA, Luo S, Chiong R (2020) Deep learning to detect Alzheimer\u2019s disease from neuroimaging: a systematic literature review. Comput Methods Programs Biomed 187:105242. https:\/\/doi.org\/10.1016\/j.cmpb.2019.105242","journal-title":"Comput Methods Programs Biomed"},{"issue":"1","key":"252_CR23","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/s40708-015-0027-x","volume":"3","author":"C Zheng","year":"2016","unstructured":"Zheng C, Xia Y, Pan Y, Chen J (2016) Automated identification of dementia using medical imaging: a survey from a pattern classification perspective. Brain Inform 3(1):17\u201327. https:\/\/doi.org\/10.1007\/s40708-015-0027-x","journal-title":"Brain Inform"},{"key":"252_CR24","doi-asserted-by":"publisher","unstructured":"Cui R, Liu M, Li G (2018) Longitudinal analysis for Alzheimer\u2019s disease diagnosis using RNN. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC: IEEE, pp. 1398\u20131401. https:\/\/doi.org\/10.1109\/ISBI.2018.8363833","DOI":"10.1109\/ISBI.2018.8363833"},{"key":"252_CR25","doi-asserted-by":"publisher","unstructured":"Esmaeilzadeh S, Belivanis DI, Pohl KM, Adeli E (2018) End-to-end Alzheimer\u2019s disease diagnosis and biomarker identification. In Shi Y, Suk H-I, Liu M (Eds) Machine Learning in Medical Imaging, in Lecture Notes in Computer Science, vol. 11046, Cham: Springer International Publishing, pp. 337\u2013345. https:\/\/doi.org\/10.1007\/978-3-030-00919-9_39","DOI":"10.1007\/978-3-030-00919-9_39"},{"key":"252_CR26","doi-asserted-by":"publisher","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), Phoenix, AZ, USA: IEEE, pp. 126\u2013130. https:\/\/doi.org\/10.1109\/ICIP.2016.7532332","DOI":"10.1109\/ICIP.2016.7532332"},{"key":"252_CR27","doi-asserted-by":"publisher","unstructured":"Karasawa H, Liu C-L, Ohwada H (2018) Deep 3D convolutional neural network architectures for Alzheimer\u2019s disease diagnosis. In Intelligent information and database systems. In: Nguyen NT, Hoang DH, Hong T-P, Pham H, Trawi\u0144ski B (Eds) Lecture Notes in Computer Science, vol. 10751. Cham: Springer International Publishing, pp. 287\u2013296. https:\/\/doi.org\/10.1007\/978-3-319-75417-8_27","DOI":"10.1007\/978-3-319-75417-8_27"},{"key":"252_CR28","doi-asserted-by":"publisher","unstructured":"Korolev S, Safiullin A, Belyaev M, Dodonova Y (2017) Residual and plain convolutional neural networks for 3D brain MRI classification. In 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, Australia: IEEE, pp. 835\u2013838. https:\/\/doi.org\/10.1109\/ISBI.2017.7950647","DOI":"10.1109\/ISBI.2017.7950647"},{"key":"252_CR29","doi-asserted-by":"publisher","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), Beijing: IEEE, pp. 1\u20135. https:\/\/doi.org\/10.1109\/IST.2017.8261566","DOI":"10.1109\/IST.2017.8261566"},{"key":"252_CR30","doi-asserted-by":"publisher","unstructured":"Payan A, Montana G (2015) Predicting Alzheimer\u2019s disease: a neuroimaging study with 3D convolutional neural networks. https:\/\/doi.org\/10.48550\/ARXIV.1502.02506","DOI":"10.48550\/ARXIV.1502.02506"},{"key":"252_CR31","doi-asserted-by":"publisher","unstructured":"Tomassini S, Falcionelli N, Sernani P, Muller H, Dragoni AF (2021) An end-to-end 3D ConvLSTM-based framework for early diagnosis of Alzheimer\u2019s disease from full-resolution whole-brain sMRI scans. In 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), Aveiro, Portugal: IEEE, pp. 74\u201378. https:\/\/doi.org\/10.1109\/CBMS52027.2021.00081","DOI":"10.1109\/CBMS52027.2021.00081"},{"key":"252_CR32","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 et al (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"},{"key":"252_CR33","first-page":"1571","volume":"2018","author":"C Yang","year":"2018","unstructured":"Yang C, Rangarajan A, Ranka S (2018) Visual explanations from deep 3D convolutional neural networks for Alzheimer\u2019s disease classification. AMIA Annu Symp Proc AMIA Symp 2018:1571\u20131580","journal-title":"AMIA Annu Symp Proc AMIA Symp"},{"issue":"1","key":"252_CR34","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(1):71\u201386. https:\/\/doi.org\/10.1007\/s12021-019-09419-w","journal-title":"Neuroinformatics"},{"key":"252_CR35","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.7.5.056001","author":"H Parmar","year":"2020","unstructured":"Parmar H, Nutter B, Long R, Antani S, Mitra S (2020) Spatiotemporal feature extraction and classification of Alzheimer\u2019s disease using deep learning 3D-CNN for fMRI data. J Med Imaging. https:\/\/doi.org\/10.1117\/1.JMI.7.5.056001","journal-title":"J Med Imaging"},{"key":"252_CR36","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1007\/978-3-030-00320-3_17","volume-title":"PRedictive Intelligence in MEdicine: First International Workshop, PRIME 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings","author":"C Feng","year":"2018","unstructured":"Feng C, Elazab A, Yang P, Wang T, Lei B, Xiao X (2018) 3D convolutional neural network and stacked bidirectional recurrent neural network for Alzheimer\u2019s disease diagnosis. In: Rekik I, Unal G, Adeli E, Park SH (eds) PRedictive Intelligence in MEdicine: First International Workshop, PRIME 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings. Springer International Publishing, Cham, pp 138\u2013146. https:\/\/doi.org\/10.1007\/978-3-030-00320-3_17"},{"key":"252_CR37","doi-asserted-by":"publisher","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), Shanghai: IEEE, pp. 1\u20135. https:\/\/doi.org\/10.1109\/CISP-BMEI.2017.8302281","DOI":"10.1109\/CISP-BMEI.2017.8302281"},{"key":"252_CR38","doi-asserted-by":"publisher","unstructured":"Aderghal K, Benois-Pineau J, Afdel K, Gwena\u00eblle C (2017) FuseMe: classification of sMRI images by fusion of Deep CNNs in 2D+\u03b5 projections. In Proceedings of the 15th international workshop on content-based multimedia indexing, Florence Italy: ACM, pp. 1\u20137. https:\/\/doi.org\/10.1145\/3095713.3095749","DOI":"10.1145\/3095713.3095749"},{"key":"252_CR39","doi-asserted-by":"publisher","unstructured":"Aderghal K, Manuel B, Benois-Pineau J, Gwena\u00ealle C, Afdel K (2017) Classification of sMRI for AD diagnosis with convolutional neuronal networks: a pilot 2-D+\u03f5 study on ADNI. presented at the International Conference, MMM 2017, http:\/\/www.springer.com\/, pp 690. https:\/\/doi.org\/10.1007\/978-3-319-51811-4_56","DOI":"10.1007\/978-3-319-51811-4_56"},{"key":"252_CR40","doi-asserted-by":"publisher","unstructured":"Billones CD, Demetria OJLD, Hostallero DED, Naval PC (2016) DemNet: a convolutional neural network for the detection of Alzheimer\u2019s disease and mild cognitive impairment. In 2016 IEEE Region 10 Conference (TENCON), Singapore: IEEE, pp. 3724\u20133727. https:\/\/doi.org\/10.1109\/TENCON.2016.7848755","DOI":"10.1109\/TENCON.2016.7848755"},{"key":"252_CR41","doi-asserted-by":"publisher","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), Wuxi: IEEE, pp. 1\u20134. https:\/\/doi.org\/10.1109\/ISC2.2017.8090871","DOI":"10.1109\/ISC2.2017.8090871"},{"key":"252_CR42","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1007\/978-3-319-96890-2_27","volume-title":"Web and big data: second international joint conference, APWeb-WAIM 2018, Macau, China, July 23\u201325, 2018, Proceedings, Part I","author":"L Gao","year":"2018","unstructured":"Gao L, Pan H, Liu F, Xie X, Zhang Z, Han J (2018) Brain disease diagnosis using deep learning features from longitudinal MR images. In: Cai Y, Ishikawa Y, Jianliang X (eds) Web and big data: second international joint conference, APWeb-WAIM 2018, Macau, China, July 23\u201325, 2018, Proceedings, Part I. Springer International Publishing, Cham, pp 327\u2013339. https:\/\/doi.org\/10.1007\/978-3-319-96890-2_27"},{"key":"252_CR43","doi-asserted-by":"publisher","first-page":"1102869","DOI":"10.3389\/fnagi.2023.1102869","volume":"15","author":"GM Hoang","year":"2023","unstructured":"Hoang GM, Kim U-H, Kim JG (2023) Vision transformers for the prediction of mild cognitive impairment to Alzheimer\u2019s disease progression using mid-sagittal sMRI. Front Aging Neurosci 15:1102869. https:\/\/doi.org\/10.3389\/fnagi.2023.1102869","journal-title":"Front Aging Neurosci"},{"key":"252_CR44","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1007\/978-3-030-05587-5_34","volume-title":"Brain informatics, Lecture Notes in Computer Science","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. In: Wang S, Yamamoto V, Jianzhong S, Yang Y, Jones E, Iasemidis L, Mitchell T (eds) Brain informatics, Lecture Notes in Computer Science. Springer International Publishing, Cham, pp 359\u2013369. https:\/\/doi.org\/10.1007\/978-3-030-05587-5_34"},{"key":"252_CR45","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":"252_CR46","doi-asserted-by":"publisher","unstructured":"Kushol R, Masoumzadeh A, Huo D, Kalra S, Yang Y-H (2022) Addformer: Alzheimer\u2019s disease detection from structural MRI using fusion transformer. In 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), Kolkata, India: IEEE, pp. 1\u20135. https:\/\/doi.org\/10.1109\/ISBI52829.2022.9761421","DOI":"10.1109\/ISBI52829.2022.9761421"},{"key":"252_CR47","doi-asserted-by":"publisher","unstructured":"Lyu Y, Yu X, Zhu D, Zhang L (2022) Classification of Alzheimer\u2019s disease via vision transformer: classification of Alzheimer\u2019s disease via vision transformer. In Proceedings of the 15th international conference on PErvasive technologies related to assistive environments, Corfu Greece: ACM, pp. 463\u2013468. https:\/\/doi.org\/10.1145\/3529190.3534754","DOI":"10.1145\/3529190.3534754"},{"key":"252_CR48","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 et al (2021) A transfer learning approach for early diagnosis of Alzheimer\u2019s disease on MRI images. Neuroscience 460:43\u201352. https:\/\/doi.org\/10.1016\/j.neuroscience.2021.01.002","journal-title":"Neuroscience"},{"key":"252_CR49","doi-asserted-by":"publisher","unstructured":"Moussavi-Khalkhali A, Jamshidi M, Wijemanne S (2016) Feature fusion for denoising and sparse autoencoders: application to neuroimaging data. In 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), Anaheim, CA, USA: IEEE, pp. 605\u2013610. https:\/\/doi.org\/10.1109\/ICMLA.2016.0106","DOI":"10.1109\/ICMLA.2016.0106"},{"key":"252_CR50","doi-asserted-by":"publisher","unstructured":"Sarraf S, DeSouza DD, Anderson J, Tofighi G, The A. D. N. Initiativ (2017) DeepAD: Alzheimer\u2019s disease classification via deep convolutional neural networks using MRI and fMRI. bioRxiv. https:\/\/doi.org\/10.1101\/070441","DOI":"10.1101\/070441"},{"key":"252_CR51","unstructured":"Sarraf S, Tofighi G (2017) Classification of Alzheimer\u2019s disease structural MRI data by deep learning convolutional neural networks. arXiv: arXiv:1607.06583. Accessed 30 Dec 2023. http:\/\/arxiv.org\/abs\/1607.06583"},{"key":"252_CR52","doi-asserted-by":"publisher","unstructured":"Valliani A, Soni A (2017) Deep residual nets for improved Alzheimer\u2019s diagnosis. In Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, Boston Massachusetts USA: ACM, pp. 615\u2013615. https:\/\/doi.org\/10.1145\/3107411.3108224","DOI":"10.1145\/3107411.3108224"},{"issue":"10","key":"252_CR53","doi-asserted-by":"publisher","first-page":"992","DOI":"10.21037\/qims.2018.10.17","volume":"8","author":"C Wu","year":"2018","unstructured":"Wu C, Guo S, Hong Y, Xiao B, Wu Y, Zhang Q (2018) Discrimination and conversion prediction of mild cognitive impairment using convolutional neural networks. Quant Imaging Med Surg 8(10):992\u20131003. https:\/\/doi.org\/10.21037\/qims.2018.10.17","journal-title":"Quant Imaging Med Surg"},{"key":"252_CR54","doi-asserted-by":"publisher","unstructured":"Kazemi Y, Houghten S (2018) A deep learning pipeline to classify different stages of Alzheimer\u2019s disease from fMRI data. In 2018 IEEE conference on computational intelligence in bioinformatics and computational biology (CIBCB), St. Louis, MO: IEEE, pp. 1\u20138. https:\/\/doi.org\/10.1109\/CIBCB.2018.8404980","DOI":"10.1109\/CIBCB.2018.8404980"},{"key":"252_CR55","doi-asserted-by":"publisher","unstructured":"Sarraf S, Tofighi G (2016) Deep learning-based pipeline to recognize Alzheimer\u2019s disease using fMRI data. In 2016 Future Technologies Conference (FTC), San Francisco, CA, USA: IEEE, pp. 816\u2013820. https:\/\/doi.org\/10.1109\/FTC.2016.7821697","DOI":"10.1109\/FTC.2016.7821697"},{"issue":"2","key":"252_CR56","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/s10916-019-1475-2","volume":"44","author":"F Ramzan","year":"2020","unstructured":"Ramzan F et al (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):37. https:\/\/doi.org\/10.1007\/s10916-019-1475-2","journal-title":"J Med Syst"},{"key":"252_CR57","doi-asserted-by":"publisher","first-page":"35","DOI":"10.3389\/fninf.2018.00035","volume":"12","author":"M Liu","year":"2018","unstructured":"Liu M, Cheng D, Yan W, Alzheimer\u2019s Disease Neuroimaging Initiative (2018) Classification of Alzheimer\u2019s disease by combination of convolutional and recurrent neural networks using FDG-PET images. Front Neuroinform 12:35. https:\/\/doi.org\/10.3389\/fninf.2018.00035","journal-title":"Front Neuroinform"},{"issue":"4","key":"252_CR58","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"},{"issue":"07","key":"252_CR59","doi-asserted-by":"publisher","first-page":"1650025","DOI":"10.1142\/S0129065716500258","volume":"26","author":"A Ortiz","year":"2016","unstructured":"Ortiz A, Munilla J, G\u00f3rriz JM, Ram\u00edrez J (2016) Ensembles of deep learning architectures for the early diagnosis of the Alzheimer\u2019s disease. Int J Neural Syst 26(07):1650025. https:\/\/doi.org\/10.1142\/S0129065716500258","journal-title":"Int J Neural Syst"},{"key":"252_CR60","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1007\/978-3-319-60964-5_36","volume-title":"Medical image understanding and analysis","author":"A Ortiz","year":"2017","unstructured":"Ortiz A, Munilla J, Mart\u00ednez-Murcia FJ, G\u00f3rriz JM, Ram\u00edrez J (2017) Learning longitudinal MRI patterns by SICE and deep learning: assessing the Alzheimer\u2019s disease progression. In: Hern\u00e1ndez MV, Gonz\u00e1lez-Castro V (eds) Medical image understanding and analysis. Springer International Publishing, Cham, pp 413\u2013424. https:\/\/doi.org\/10.1007\/978-3-319-60964-5_36"},{"key":"252_CR61","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.media.2017.01.008","volume":"37","author":"H-I Suk","year":"2017","unstructured":"Suk H-I, Lee S-W, Shen D (2017) Deep ensemble learning of sparse regression models for brain disease diagnosis. Med Image Anal 37:101\u2013113. https:\/\/doi.org\/10.1016\/j.media.2017.01.008","journal-title":"Med Image Anal"},{"issue":"10","key":"252_CR62","doi-asserted-by":"publisher","first-page":"5019","DOI":"10.1002\/hbm.23711","volume":"38","author":"X Chen","year":"2017","unstructured":"Chen X, Zhang H, Zhang L, Shen C, Lee S, Shen D (2017) Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification. Hum Brain Mapp 38(10):5019\u20135034. https:\/\/doi.org\/10.1002\/hbm.23711","journal-title":"Hum Brain Mapp"},{"key":"252_CR63","doi-asserted-by":"publisher","first-page":"615","DOI":"10.3389\/fnins.2017.00615","volume":"11","author":"H Guo","year":"2017","unstructured":"Guo H, Zhang F, Chen J, Xu Y, Xiang J (2017) Machine learning classification combining multiple features of a hyper-network of fMRI data in Alzheimer\u2019s disease. Front Neurosci 11:615. https:\/\/doi.org\/10.3389\/fnins.2017.00615","journal-title":"Front Neurosci"},{"key":"252_CR64","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.jneumeth.2017.03.006","volume":"282","author":"SH Hojjati","year":"2017","unstructured":"Hojjati SH, Ebrahimzadeh A, Khazaee A, Babajani-Feremi A (2017) Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM. J Neurosci Methods 282:69\u201380. https:\/\/doi.org\/10.1016\/j.jneumeth.2017.03.006","journal-title":"J Neurosci Methods"},{"key":"252_CR65","doi-asserted-by":"publisher","unstructured":"Hu C, Ju R, Shen Y, Zhou P, Li Q (2016) Clinical decision support for Alzheimer\u2019s disease based on deep learning and brain network. In 2016 IEEE International Conference on Communications (ICC), Kuala Lumpur, Malaysia: IEEE, pp. 1\u20136. https:\/\/doi.org\/10.1109\/ICC.2016.7510831","DOI":"10.1109\/ICC.2016.7510831"},{"issue":"1","key":"252_CR66","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 Bioinform 16(1):244\u2013257. https:\/\/doi.org\/10.1109\/TCBB.2017.2776910","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"key":"252_CR67","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.neulet.2017.10.052","volume":"664","author":"L Qian","year":"2018","unstructured":"Qian L, Zheng L, Shang Y, Zhang Y, Zhang Y (2018) Intrinsic frequency specific brain networks for identification of MCI individuals using resting-state fMRI. Neurosci Lett 664:7\u201314. https:\/\/doi.org\/10.1016\/j.neulet.2017.10.052","journal-title":"Neurosci Lett"},{"issue":"9","key":"252_CR68","doi-asserted-by":"publisher","first-page":"3102","DOI":"10.3390\/s22093102","volume":"22","author":"A Sadiq","year":"2022","unstructured":"Sadiq A, Yahya N, Tang TB, Hashim H, Naseem I (2022) Wavelet-based fractal analysis of rs-fMRI for classification of Alzheimer\u2019s disease. Sensors 22(9):3102. https:\/\/doi.org\/10.3390\/s22093102","journal-title":"Sensors"},{"key":"252_CR69","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1016\/j.neuroimage.2016.01.005","volume":"129","author":"H-I Suk","year":"2016","unstructured":"Suk H-I, Wee C-Y, Lee S-W, Shen D (2016) State-space model with deep learning for functional dynamics estimation in resting-state fMRI. Neuroimage 129:292\u2013307. https:\/\/doi.org\/10.1016\/j.neuroimage.2016.01.005","journal-title":"Neuroimage"},{"issue":"5","key":"252_CR70","doi-asserted-by":"publisher","first-page":"2370","DOI":"10.1002\/hbm.23524","volume":"38","author":"R Yu","year":"2017","unstructured":"Yu R, Zhang H, An L, Chen X, Wei Z, Shen D (2017) Connectivity strength-weighted sparse group representation-based brain network construction for M CI classification. Hum Brain Mapp 38(5):2370\u20132383. https:\/\/doi.org\/10.1002\/hbm.23524","journal-title":"Hum Brain Mapp"},{"key":"252_CR71","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2020.558434","volume":"14","author":"L Zhang","year":"2020","unstructured":"Zhang L et al (2020) Investigation on the alteration of brain functional network and its role in the identification of mild cognitive impairment. Front Neurosci 14:558434. https:\/\/doi.org\/10.3389\/fnins.2020.558434","journal-title":"Front Neurosci"},{"issue":"7","key":"252_CR72","doi-asserted-by":"publisher","first-page":"6822","DOI":"10.1109\/TCYB.2020.3016953","volume":"52","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Zhang H, Adeli E, Chen X, Liu M, Shen D (2022) Multiview feature learning with multiatlas-based functional connectivity networks for MCI diagnosis. IEEE Trans Cybern 52(7):6822\u20136833. https:\/\/doi.org\/10.1109\/TCYB.2020.3016953","journal-title":"IEEE Trans Cybern"},{"issue":"10","key":"252_CR73","doi-asserted-by":"publisher","DOI":"10.1002\/brb3.1407","volume":"9","author":"J Zhao","year":"2019","unstructured":"Zhao J, Ding X, Du Y, Wang X, Men G (2019) Functional connectivity between white matter and gray matter based on fMRI for Alzheimer\u2019s disease classification. Brain Behav 9(10):e01407. https:\/\/doi.org\/10.1002\/brb3.1407","journal-title":"Brain Behav"},{"key":"252_CR74","doi-asserted-by":"publisher","unstructured":"Zheng C, Xia Y, Chen Y, Yin X, Zhang Y (2018) Early Diagnosis of Alzheimer\u2019s Disease by Ensemble Deep Learning Using FDG-PET. In Peng Y, Yu K, Lu J, Jiang X (Eds) Intelligence science and big data engineering, in Lecture Notes in Computer Science. Cham: Springer International Publishing, 2018, pp. 614\u2013622. https:\/\/doi.org\/10.1007\/978-3-030-02698-1_53","DOI":"10.1007\/978-3-030-02698-1_53"},{"issue":"4","key":"252_CR75","doi-asserted-by":"publisher","first-page":"1132","DOI":"10.1109\/TBME.2014.2372011","volume":"62","author":"S Liu","year":"2015","unstructured":"Liu S et al (2015) Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer\u2019s disease. IEEE Trans Biomed Eng 62(4):1132\u20131140. https:\/\/doi.org\/10.1109\/TBME.2014.2372011","journal-title":"IEEE Trans Biomed Eng"},{"key":"252_CR76","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1016\/j.neucom.2020.03.006","volume":"400","author":"J Liu","year":"2020","unstructured":"Liu J, Pan Y, Wu F-X, Wang J (2020) Enhancing the feature representation of multi-modal MRI data by combining multi-view information for MCI classification. Neurocomputing 400:322\u2013332. https:\/\/doi.org\/10.1016\/j.neucom.2020.03.006","journal-title":"Neurocomputing"},{"key":"252_CR77","doi-asserted-by":"publisher","unstructured":"Cui R, Liu M (2018) Hippocampus analysis based on 3D CNN for Alzheimer\u2019s disease diagnosis. In Jiang X, Hwang JN (Eds) Tenth International Conference on Digital Image Processing (ICDIP 2018), Shanghai, China: SPIE, Aug. 2018, p. 275. https:\/\/doi.org\/10.1117\/12.2503194","DOI":"10.1117\/12.2503194"},{"key":"252_CR78","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 (2018) Alzheimer\u2019s disease diagnosis based on multiple cluster dense convolutional networks. Comput Med Imaging Graph 70:101\u2013110. https:\/\/doi.org\/10.1016\/j.compmedimag.2018.09.009","journal-title":"Comput Med Imaging Graph"},{"key":"252_CR79","doi-asserted-by":"publisher","first-page":"777","DOI":"10.3389\/fnins.2018.00777","volume":"12","author":"W Lin","year":"2018","unstructured":"Lin W et al (2018) Convolutional neural networks-based MRI image analysis for the Alzheimer\u2019s disease prediction from mild cognitive impairment. Front Neurosci 12:777. https:\/\/doi.org\/10.3389\/fnins.2018.00777","journal-title":"Front Neurosci"},{"key":"252_CR80","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 Recognit 63:487\u2013498. https:\/\/doi.org\/10.1016\/j.patcog.2016.09.032","journal-title":"Pattern Recognit"},{"key":"252_CR81","doi-asserted-by":"publisher","unstructured":"Cheng D, Liu M, Fu J, Wang Y (2017) Classification of MR brain images by combination of multi-CNNs for AD diagnosis. In: Falco CM, Jiang X (Eds) Presented at the Ninth International Conference on Digital Image Processing (ICDIP 2017), Hong Kong, China, p. 1042042. https:\/\/doi.org\/10.1117\/12.2281808","DOI":"10.1117\/12.2281808"},{"key":"252_CR82","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 (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"},{"issue":"20","key":"252_CR83","doi-asserted-by":"publisher","first-page":"6825","DOI":"10.1007\/s00500-018-3421-5","volume":"22","author":"T-D Vu","year":"2018","unstructured":"Vu T-D, Ho N-H, Yang H-J, Kim J, Song H-C (2018) Non-white matter tissue extraction and deep convolutional neural network for Alzheimer\u2019s disease detection. Soft Comput 22(20):6825\u20136833. https:\/\/doi.org\/10.1007\/s00500-018-3421-5","journal-title":"Soft Comput"},{"issue":"1","key":"252_CR84","doi-asserted-by":"publisher","first-page":"Art 1","DOI":"10.1038\/s41598-018-22871-z","volume":"8","author":"D Lu","year":"2018","unstructured":"Lu D, Popuri K, Ding GW, Balachandar R, Beg MF (2018) Multimodal and multiscale deep neural networks for the early diagnosis of Alzheimer\u2019s disease using structural MR and FDG-PET images. Sci Rep 8(1):Art 1. https:\/\/doi.org\/10.1038\/s41598-018-22871-z","journal-title":"Sci Rep"},{"key":"252_CR85","doi-asserted-by":"publisher","unstructured":"Vu TD, Yang H-J, Nguyen VQ, Oh A-R, Kim M-S (2017) Multimodal learning using convolution neural network and Sparse Autoencoder. In 2017 IEEE international conference on big data and smart computing (BigComp), Jeju: IEEE, pp. 309\u2013312. https:\/\/doi.org\/10.1109\/BIGCOMP.2017.7881683","DOI":"10.1109\/BIGCOMP.2017.7881683"},{"key":"252_CR86","unstructured":"Alpaydin E (2016) Machine learning: the new AI. MIT Press"},{"key":"252_CR87","doi-asserted-by":"publisher","unstructured":"Caruana R, Niculescu-Mizil A (2006) An empirical comparison of supervised learning algorithms. In Proceedings of the 23rd international conference on Machine learning\u2014ICML \u201906, Pittsburgh, Pennsylvania: ACM Press, 2006, pp. 161\u2013168. https:\/\/doi.org\/10.1145\/1143844.1143865","DOI":"10.1145\/1143844.1143865"},{"key":"252_CR88","doi-asserted-by":"crossref","unstructured":"Hinton G, Sejnowski TJ (1999) Unsupervised learning: foundations of neural computation. MIT Press","DOI":"10.7551\/mitpress\/7011.001.0001"},{"issue":"7553","key":"252_CR89","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444. https:\/\/doi.org\/10.1038\/nature14539","journal-title":"Nature"},{"issue":"6088","key":"252_CR90","doi-asserted-by":"publisher","first-page":"6088","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):6088. https:\/\/doi.org\/10.1038\/323533a0","journal-title":"Nature"},{"issue":"1","key":"252_CR91","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1109\/72.655045","volume":"9","author":"G-B Huang","year":"1998","unstructured":"Huang G-B, Babri HA (1998) Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions. IEEE Trans Neural Netw 9(1):224\u2013229. https:\/\/doi.org\/10.1109\/72.655045","journal-title":"IEEE Trans Neural Netw"},{"key":"252_CR92","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85\u2013117. https:\/\/doi.org\/10.1016\/j.neunet.2014.09.003","journal-title":"Neural Netw"},{"key":"252_CR93","unstructured":"Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th International Conference on International Conference on Machine Learning, in ICML\u201910. Madison, WI, USA: Omnipress, pp. 807\u2013814"},{"key":"252_CR94","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep Learning. MIT Press"},{"key":"252_CR95","unstructured":"Glorot X, Bengio Y (2023) Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, JMLR Workshop and Conference Proceedings, Mar. 2010, pp. 249\u2013256. https:\/\/proceedings.mlr.press\/v9\/glorot10a.html. Accessed 21 Mar 2023"},{"issue":"14","key":"252_CR96","doi-asserted-by":"publisher","first-page":"2627","DOI":"10.1016\/S1352-2310(97)00447-0","volume":"32","author":"MW Gardner","year":"1998","unstructured":"Gardner MW, Dorling SR (1998) Artificial neural networks (the multilayer perceptron)\u2014a review of applications in the atmospheric sciences. Atmos Environ 32(14):2627\u20132636. https:\/\/doi.org\/10.1016\/S1352-2310(97)00447-0","journal-title":"Atmos Environ"},{"issue":"5","key":"252_CR97","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","volume":"2","author":"K Hornik","year":"1989","unstructured":"Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2(5):359\u2013366. https:\/\/doi.org\/10.1016\/0893-6080(89)90020-8","journal-title":"Neural Netw"},{"issue":"7","key":"252_CR98","doi-asserted-by":"publisher","first-page":"1473","DOI":"10.1109\/TBME.2015.2496233","volume":"63","author":"M Liu","year":"2016","unstructured":"Liu M, Zhang D, Adeli E, Shen D (2016) Inherent structure-based multiview learning with multitemplate feature representation for Alzheimer\u2019s disease diagnosis. IEEE Trans Biomed Eng 63(7):1473\u20131482. https:\/\/doi.org\/10.1109\/TBME.2015.2496233","journal-title":"IEEE Trans Biomed Eng"},{"issue":"12","key":"252_CR99","doi-asserted-by":"publisher","first-page":"2524","DOI":"10.1109\/TMI.2016.2582386","volume":"35","author":"J Zhang","year":"2016","unstructured":"Zhang J, Gao Y, Gao Y, Munsell BC, Shen D (2016) Detecting anatomical landmarks for fast Alzheimer\u2019s disease diagnosis. IEEE Trans Med Imaging 35(12):2524\u20132533. https:\/\/doi.org\/10.1109\/TMI.2016.2582386","journal-title":"IEEE Trans Med Imaging"},{"issue":"12","key":"252_CR100","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1038\/nbt1206-1565","volume":"24","author":"WS Noble","year":"2006","unstructured":"Noble WS (2006) What is a support vector machine? Nat Biotechnol 24(12):1565\u20131567. https:\/\/doi.org\/10.1038\/nbt1206-1565","journal-title":"Nat Biotechnol"},{"key":"252_CR101","doi-asserted-by":"publisher","unstructured":"Parmar A, Katariya R, Patel V (2019) A review on random forest: an ensemble classifier. In Hemanth J, Fernando X, Lafata P, Baig Z (Eds.) International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI) 2018, vol. 26, in Lecture Notes on Data Engineering and Communications Technologies, Cham: Springer International Publishing, 2019, pp. 758\u2013763. https:\/\/doi.org\/10.1007\/978-3-030-03146-6_86","DOI":"10.1007\/978-3-030-03146-6_86"},{"issue":"4","key":"252_CR102","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun Y et al (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541\u2013551. https:\/\/doi.org\/10.1162\/neco.1989.1.4.541","journal-title":"Neural Comput"},{"issue":"8","key":"252_CR103","doi-asserted-by":"publisher","first-page":"5455","DOI":"10.1007\/s10462-020-09825-6","volume":"53","author":"A Khan","year":"2020","unstructured":"Khan A, Sohail A, Zahoora U, Qureshi AS (2020) A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev 53(8):5455\u20135516. https:\/\/doi.org\/10.1007\/s10462-020-09825-6","journal-title":"Artif Intell Rev"},{"key":"252_CR104","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60\u201388. https:\/\/doi.org\/10.1016\/j.media.2017.07.005","journal-title":"Med Image Anal"},{"key":"252_CR105","doi-asserted-by":"publisher","unstructured":"Razzak MI, Naz S, Zaib A (2018) deep learning for medical image processing: overview, challenges and the future. In Dey N, Ashour AS, Borra S (Eds) Classification in BioApps: automation of decision making, in Lecture Notes in Computational Vision and Biomechanics. Cham: Springer International Publishing, pp. 323\u2013350. https:\/\/doi.org\/10.1007\/978-3-319-65981-7_12","DOI":"10.1007\/978-3-319-65981-7_12"},{"issue":"1","key":"252_CR106","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","volume":"8","author":"L Alzubaidi","year":"2021","unstructured":"Alzubaidi L et al (2021) Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8(1):53. https:\/\/doi.org\/10.1186\/s40537-021-00444-8","journal-title":"J Big Data"},{"key":"252_CR107","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In Advances in neural information processing systems, Curran Associates, Inc. https:\/\/papers.nips.cc\/paper_files\/paper\/2012\/hash\/c399862d3b9d6b76c8436e924a68c45b-Abstract.html . Accessed 21 Mar 2023"},{"key":"252_CR108","doi-asserted-by":"publisher","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. arXiv: arXiv:1409.1556. https:\/\/doi.org\/10.48550\/arXiv.1409.1556","DOI":"10.48550\/arXiv.1409.1556"},{"key":"252_CR109","doi-asserted-by":"publisher","unstructured":"Jia Y et al (2014) Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the 22nd ACM international conference on Multimedia, in MM \u201914. New York, NY, USA: Association for Computing Machinery, pp. 675\u2013678. https:\/\/doi.org\/10.1145\/2647868.2654889","DOI":"10.1145\/2647868.2654889"},{"key":"252_CR110","doi-asserted-by":"crossref","unstructured":"Szegedy C et al (2015) Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1\u20139. https:\/\/www.cv-foundation.org\/openaccess\/content_cvpr_2015\/html\/Szegedy_Going_Deeper_With_2015_CVPR_paper.html . Accessed 12 Sep 2024","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"252_CR111","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700\u20134708. http:\/\/openaccess.thecvf.com\/content_cvpr_2017\/html\/Huang_Densely_Connected_Convolutional_CVPR_2017_paper.html. Accessed 12 Sep 2024","DOI":"10.1109\/CVPR.2017.243"},{"issue":"11","key":"252_CR112","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"issue":"1","key":"252_CR113","doi-asserted-by":"publisher","first-page":"Art 1","DOI":"10.1609\/aaai.v31i1.11231","volume":"31","author":"C Szegedy","year":"2017","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2017) Inception-v4, inception-ResNet and the impact of residual connections on learning. Proc AAAI Conf Artif Intell. 31(1):Art 1. https:\/\/doi.org\/10.1609\/aaai.v31i1.11231","journal-title":"Proc AAAI Conf Artif Intell."},{"key":"252_CR114","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. arXiv: arXiv:1512.03385. https:\/\/doi.org\/10.48550\/arXiv.1512.03385","DOI":"10.48550\/arXiv.1512.03385"},{"key":"252_CR115","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","volume":"187","author":"Y Guo","year":"2016","unstructured":"Guo Y, Liu Y, Oerlemans A, Lao S, Wu S, Lew MS (2016) Deep learning for visual understanding: a review. Neurocomputing 187:27\u201348. https:\/\/doi.org\/10.1016\/j.neucom.2015.09.116","journal-title":"Neurocomputing"},{"key":"252_CR116","unstructured":"Lipton ZC, Berkowitz J, Elkan C (2015) A critical review of recurrent neural networks for sequence learning. arXiv: arXiv:1506.00019. http:\/\/arxiv.org\/abs\/1506.00019. Accessed 2 Jan 2024"},{"key":"252_CR117","unstructured":"Schmidt RM (2019) Recurrent Neural Networks (RNNs): a gentle introduction and overview. arXiv: arXiv:1912.05911. http:\/\/arxiv.org\/abs\/1912.05911. Accessed 3 Jan 2024"},{"issue":"5786","key":"252_CR118","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504\u2013507. https:\/\/doi.org\/10.1126\/science.1127647","journal-title":"Science"},{"issue":"4","key":"252_CR119","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1007\/s00422-022-00937-6","volume":"116","author":"H Bourlard","year":"2022","unstructured":"Bourlard H, Kabil SH (2022) Autoencoders reloaded. Biol Cybern 116(4):389\u2013406. https:\/\/doi.org\/10.1007\/s00422-022-00937-6","journal-title":"Biol Cybern"},{"key":"252_CR120","doi-asserted-by":"publisher","unstructured":"Vincent P, Larochelle H, Bengio Y, Manzagol P-A (2008) Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th international conference on Machine learning - ICML \u201908, Helsinki, Finland: ACM Press, pp. 1096\u20131103. https:\/\/doi.org\/10.1145\/1390156.1390294","DOI":"10.1145\/1390156.1390294"},{"key":"252_CR121","doi-asserted-by":"publisher","unstructured":"Masci J, Meier U, Cire\u015fan D, Schmidhuber J (2011) Stacked convolutional auto-encoders for hierarchical feature extraction. In Honkela T, Duch W, Girolami M, Kaski S (Eds) Artificial neural networks and machine learning\u2014ICANN 2011, vol. 6791, in Lecture Notes in Computer Science, vol. 6791, Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 52\u201359. https:\/\/doi.org\/10.1007\/978-3-642-21735-7_7","DOI":"10.1007\/978-3-642-21735-7_7"},{"key":"252_CR122","doi-asserted-by":"publisher","unstructured":"Liu S, Liu S, Cai W, Pujol S, Kikinis R, Feng DD (2015) Multi-phase feature representation learning for neurodegenerative disease diagnosis. In Chalup SK, Blair AD, Randall M (Eds) Artificial life and computational intelligence. in Lecture Notes in Computer Science. Cham: Springer International Publishing, pp. 350\u2013359. https:\/\/doi.org\/10.1007\/978-3-319-14803-8_27","DOI":"10.1007\/978-3-319-14803-8_27"},{"issue":"10","key":"252_CR123","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1016\/j.tics.2007.09.004","volume":"11","author":"GE Hinton","year":"2007","unstructured":"Hinton GE (2007) Learning multiple layers of representation. Trends Cogn Sci 11(10):428\u2013434. https:\/\/doi.org\/10.1016\/j.tics.2007.09.004","journal-title":"Trends Cogn Sci"},{"key":"252_CR124","doi-asserted-by":"publisher","unstructured":"Singh S, Janghel RR (2022) Classification and diagnosis of Alzheimer\u2019s disease from ADNI dataset using RBM classifier. In Khanna A, Gupta D, Bhattacharyya S, Hassanien AE, Anand S, Jaiswal A (Eds) International conference on innovative computing and communications, vol. 1388, in Advances in Intelligent Systems and Computing, Singapore: Springer Singapore, pp. 517\u2013529. https:\/\/doi.org\/10.1007\/978-981-16-2597-8_44","DOI":"10.1007\/978-981-16-2597-8_44"},{"key":"252_CR125","unstructured":"Vaswani A et al (2017) Attention is all you need. In Advances in neural information processing systems, Curran Associates, Inc. https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html . Accessed 21 Mar 2023"},{"key":"252_CR126","doi-asserted-by":"publisher","unstructured":"Dosovitskiy A et al (2021) An image is worth 16x16 words: transformers for image recognition at scale. arXiv: arXiv:2010.11929. https:\/\/doi.org\/10.48550\/arXiv.2010.11929","DOI":"10.48550\/arXiv.2010.11929"},{"key":"252_CR127","doi-asserted-by":"publisher","unstructured":"Xing X, Liang G, Zhang Y, Khanal S, Lin AL, Jacobs N (2022) Advit: vision transformer on multi-modality pet images for Alzheimer disease diagnosis. In 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), Kolkata, India: IEEE, pp. 1\u20134. https:\/\/doi.org\/10.1109\/ISBI52829.2022.9761584","DOI":"10.1109\/ISBI52829.2022.9761584"},{"key":"252_CR128","doi-asserted-by":"publisher","unstructured":"Cheng D, Liu M (2017) Classification of Alzheimer\u2019s disease by cascaded convolutional neural networks using PET images. In Wang Q, Shi Y, Suk HI, Suzuki K (Eds) Machine learning in medical imaging, vol. 10541, in Lecture Notes in Computer Science, Cham: Springer International Publishing, pp. 106\u2013113. https:\/\/doi.org\/10.1007\/978-3-319-67389-9_13","DOI":"10.1007\/978-3-319-67389-9_13"},{"key":"252_CR129","unstructured":"AIBL https:\/\/aibl.csiro.au\/. Accessed 5 Jun 2023"},{"key":"252_CR130","unstructured":"National Alzheimer\u2019s Coordinating Center. https:\/\/naccdata.org\/. Accessed 5 Jun 2023"}],"container-title":["Brain Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40708-025-00252-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40708-025-00252-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40708-025-00252-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,21]],"date-time":"2025-03-21T15:55:14Z","timestamp":1742572514000},"score":1,"resource":{"primary":{"URL":"https:\/\/braininformatics.springeropen.com\/articles\/10.1186\/s40708-025-00252-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,21]]},"references-count":130,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["252"],"URL":"https:\/\/doi.org\/10.1186\/s40708-025-00252-3","relation":{},"ISSN":["2198-4018","2198-4026"],"issn-type":[{"value":"2198-4018","type":"print"},{"value":"2198-4026","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,21]]},"assertion":[{"value":"29 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"8"}}