Skip to main content

Advertisement

SpringerLink
Log in

A comprehensive review on detection and classification of dementia using neuroimaging and machine learning

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Dementia, not a particular disease but rather a gathering of conditions which ascribes the debilitation of atleast two cerebrum capacities, cognitive decline and memory judgment has became ubiquous all over the world. Achieving accuracy and timely diagnosis is a major challenge in dementia detection. The recent advancements in neuroimaging techniques has setup some benchmarks. The amalgamation of computer aided algorithms with various imaging modalities proved their significance in dementia detection and classification. In this paper, a rigrous review of existing work in dementia detection and classification is presented based on the existing literature. Additionally this paper surveys the most important AI (Artificial Intellligence) and profound learning approaches for early identification of dementia.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6.
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Https://www.alz.org/alzheimers-dementia/what-is-alzheimers, What is Alzheimer’s Disease?

  2. Ahmed MR, Zhang Y, Feng Z, Lo B, Inan OT, Liao H (2019) Neuroimaging and Machine Learning for Dementia Diagnosis: Recent Advancements and Future Prospects. IEEE Rev Biomed Eng 12:19–33. https://doi.org/10.1109/RBME.2018.2886237

    Article  Google Scholar 

  3. Patro S (2019) Early Detection of Alzheimer ’ s Disease using Image Processing, vol. 8, no. 05, pp. 468–471

  4. Dening T, Sandilyan MB (2015) Dementia: definitions and types. Nurs Stand 29(37):37–42. https://doi.org/10.7748/ns.29.37.37.e9405

    Article  Google Scholar 

  5. Borroni B (2018) Biological, Neuroimaging, and Neurophysiological Markers in Frontotemporal Dementia: Three Faces of the Same Coin. J Alzheimers Dis 62(3):1113–1123. https://doi.org/10.3233/JAD-170584

    Article  Google Scholar 

  6. Jellinger KA (2018) Dementia with Lewy bodies and Parkinson’s disease-dementia: current concepts and controversies, vol. 125, no. 4. Springer Vienna, doi: https://doi.org/10.1007/s00702-017-1821-9

  7. Ghosh S, Lippa CF (2015) Clinical Subtypes of Frontotemporal Dementia. Am J Alzheimers Dis Other Dement 30(7):653–661. https://doi.org/10.1177/1533317513494442

    Article  Google Scholar 

  8. Https://www.rxlist.com/collection-of-images/x-linked_ichthyosis_picture/pictures.htm, collection of images

  9. Kim SW, Chung SJ, Oh Y-S, Yoon JH, Sunwoo MK, Hong JY, Kim J-S, Lee PH (2015) Cerebral microbleeds in patients with dementia with lewy bodies and Parkinson disease dementia. Am J Neuroradiol 36(9):1642–1647. https://doi.org/10.3174/ajnr.A4337

    Article  Google Scholar 

  10. Saeed U, Compagnone J, Aviv RI, Strafella AP, Black SE, Lang AE, Masellis M (2017) Imaging biomarkers in Parkinson’s disease and Parkinsonian syndromes: Current and emerging concepts, Transl Neurodegener, vol. 6, no. 1, doi: 10.1186/s40035-017-0076-6

  11. Billeci L, Badolato A, Bachi L, Tonacci A (2020) Machine learning for the classification of alzheimer’s disease and its prodromal stage using brain diffusion tensor imaging data: A systematic review, Processes, vol. 8, no. 9, doi: https://doi.org/10.3390/pr8091071

  12. Wen J et al. (2020) Overview of classification of Alzheimer’s disease, Med. Image Anal., vol. 63

  13. Www.medicinenet.com, medicinenet

  14. Perani D, Iaccarino L, Sala A, Caminiti SP (2017) The emerging role of PET imaging in dementia, F1000Research, vol. 6, no. 0, doi: 10.12688/f1000research.11603.1

  15. Https://www.nia.nih.gov/], NIA

  16. Tanveer M, Richhariya B, Khan RU, Rashid AH, Khanna P, Prasad M, Lin CT (2020) Machine learning techniques for the diagnosis of alzheimer’s disease: A review, ACM Trans. Multimed. Comput. Commun. Appl, vol. 16, no. 1s, doi: 10.1145/3344998

  17. Chiu SI, Lin CH, Lim WS, Chiu MJ, Chen TF, Jang JSR (2019) Predicting Neurodegenerative Diseases Using a Novel Blood Biomarkers-based Model by Machine Learning, Proc. - 2019 Int Conf Technol Appl Artif Intell. TAAI 2019, pp. 4–9, doi: 10.1109/TAAI48200.2019.8959854

  18. Altinkaya E, Polat K, B. B.-J. of the I. of Electronics, and undefined 2020, (2019) Detection of Alzheimer’s disease and dementia states based on deep learning from MRI images: a comprehensive review, Iecscience. Org, vol. 1, pp. 39–53, doi: 10.33969/JIEC.2019.11005

  19. Zhu F et al (2020) Machine Learning for the Preliminary Diagnosis of Dementia. Sci Program 1:2020. https://doi.org/10.1155/2020/5629090

    Article  Google Scholar 

  20. Alashwal H, El Halaby M, Crouse JJ, Abdalla A, Moustafa AA (2019) The application of unsupervised clustering methods to Alzheimer’s disease, Front Comput Neurosci, vol. 13, no. May, pp. 1–9, doi: 10.3389/fncom.2019.00031

  21. Cmeri C, Alzheimer ’ s Disease Classification in Brain MRI using Modified kNN Algorithm, vol. i

  22. Acharya UR et al. (2019) Automated Detection of Alzheimer’s Disease Using Brain MRI Images– A Study with Various Feature Extraction Techniques, J Med Syst, vol. 43, no. 9, doi: 10.1007/s10916-019-1428-9

  23. Gill S et al (2020) Using Machine Learning to Predict Dementia from Neuropsychiatric Symptom and Neuroimaging Data. J Alzheimers Dis 75(1):277–288. https://doi.org/10.3233/jad-191169

    Article  Google Scholar 

  24. Kumar A, Singh TR (2017) A New Decision Tree to Solve the Puzzle of Alzheimer’s Disease Pathogenesis Through Standard Diagnosis Scoring System. Interdiscip Sci Comput Life Sci 9(1):107–115. https://doi.org/10.1007/s12539-016-0144-0

    Article  MathSciNet  Google Scholar 

  25. Pettersson J (2021) The use of Decision Trees to detect Alzheimer's Disease

  26. Al-Dlaeen D, Alashqur A (2014) Using decision tree classification to assist in the prediction of Alzheimer’s disease, 2014 6th Int Conf Comput Sci Inf Technol CSIT 2014 - Proc., pp. 122–126, doi: 10.1109/CSIT.2014.6805989

  27. Sadeghi N, Foster NL, Wang AY, Minoshima S, Lieberman AP, Tasdizen T (2008) Automatic Classification of Alzheimer's Disease vs Frontotemporal Dementia : A Spatial Decision Tree Approach with FDG-PET", School of Computing , University of Utah , Salt Lake City , UT 84112 Center for Alzheimer ’ s Care , Imaging and Research , Univ, Imaging, pp. 408–411

  28. Support Vector Machine. https://en.wikipedia.org/wiki/Support-vector_machine

  29. Waghere S, RajaRajeswari P, Ganesan V (2021) Design and Implementation of System Which Efficiently Retrieve Useful Data for Detection of Dementia Disease, vol. 698. Springer Singapore, doi: 10.1007/978-981-15-7961-5_144

  30. Rajesh Kumar P, Arun Prasath T, Pallikonda Rajasekaran M, Vishnuvarthanan G (2018) Brain subject estimation using PSO K-means clustering - An automated aid for the assessment of clinical dementia, Smart Innov Syst Technol, vol. 83, no. Ictis, pp. 482–489, doi: 10.1007/978-3-319-63673-3_58

  31. Vijayalakshmi SS, Pallawi S, Genish T (2020) Alzheimer Disease Detection Using Edge Enhanced K Means Clustering Algorithm. SSRN Electron J:1–10. https://doi.org/10.2139/ssrn.3545092

  32. Lins AJCC, Muniz MTC, Bastos-Filho CJA (2019) Comparing Machine Learning Techniques for Dementia Diagnosis, 2018 IEEE Lat Am Conf Comput Intell. LA-CCI 2018, no. 1, p. 90, doi: 10.1109/LA-CCI.2018.8625209

  33. Suk H, Lee S, Shen D, Initiative N, C. Engineering (2018) Applications of chlorophyll fluorescence imaging technique in horticultural.pdf, pp. 101–113, doi: 10.1016/j.media.2017.01.008.Deep

  34. Kim J Lim J (2021) A deep neural network-based method for prediction of dementia using big data, Int J Environ Res Public Health vol. 18, no. 10, doi: 10.3390/ijerph18105386

  35. Basaia S et al (2019) Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks. NeuroImage Clin 21:101645. https://doi.org/10.1016/j.nicl.2018.101645

    Article  Google Scholar 

  36. Li F, Liu M (2018) Alzheimer’s disease diagnosis based on multiple cluster dense convolutional networks. Comput Med Imaging Graph 70:101–110. https://doi.org/10.1016/j.compmedimag.2018.09.009

    Article  Google Scholar 

  37. Spasov S, Passamonti L, Duggento A, Liò P, Toschi N (2019) A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer’s disease. Neuroimage 189:276–287. https://doi.org/10.1016/j.neuroimage.2019.01.031

    Article  Google Scholar 

  38. Wang H et al (2019) Ensemble of 3D densely connected convolutional network for diagnosis of mild cognitive impairment and Alzheimer’s disease. Neurocomputing 333:145–156. https://doi.org/10.1016/j.neucom.2018.12.018

    Article  Google Scholar 

  39. Wen J et al (2020) Convolutional neural networks for classification of Alzheimer’s disease: Overview and reproducible evaluation. Med Image Anal 63:101694. https://doi.org/10.1016/j.media.2020.101694

    Article  Google Scholar 

  40. Ieracitano C, Mammone N, Hussain A, Morabito FC (2020) A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia. Neural Netw 123:176–190. https://doi.org/10.1016/j.neunet.2019.12.006

    Article  Google Scholar 

  41. Ucuzal H, Arslan AK, Colak C (2019) Deep learning based-classification of dementia in magnetic resonance imaging scans, 2019 Int. Conf. Artif. Intell. Data Process. Symp. IDAP 2019, pp. 1–6, doi: https://doi.org/10.1109/IDAP.2019.8875961

  42. Backstrom K, Nazari M, Gu IYH, Jakola AS (2018) An efficient 3D deep convolutional network for Alzheimer’s disease diagnosis using MR images, Proc. - Int. Symp. Biomed. Imaging, vol. 2018-April, no. Isbi, pp. 149–153, oi: 10.1109/ISBI.2018.8363543

  43. Kim JP et al. (2019) Machine learning based hierarchical classification of frontotemporal dementia and Alzheimer’s disease, NeuroImage Clin, vol. 23, no. March, p. 101811, doi: 10.1016/j.nicl.2019.101811

  44. Wang SH, Phillips P, Sui Y, Liu B, Yang M, Cheng H (2018) Classification of Alzheimer’s Disease Based on Eight-Layer Convolutional Neural Network with Leaky Rectified Linear Unit and Max Pooling. J Med Syst 42(5):1–11. https://doi.org/10.1007/s10916-018-0932-7

    Article  Google Scholar 

  45. Il Suk H, Lee SW, Shen D (2014) Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. Neuroimage 101:569–582. https://doi.org/10.1016/j.neuroimage.2014.06.077

    Article  Google Scholar 

  46. Zeighami Y et al. (2019) A clinical-anatomical signature of Parkinson’s disease identified with partial least squares and magnetic resonance imaging, Neuroimage, vol. 190, no. December, pp. 69–78, doi: 10.1016/j.neuroimage.2017.12.050

  47. Canu E et al. (2017) Multiparametric MRI to distinguish early onset Alzheimer’s disease and behavioural variant of frontotemporal dementia, vol. 15. Elsevier Inc, doi: 10.1016/j.nicl.2017.05.018

  48. Bron EE, Smits M, Niessen WJ, Klein S (2015) Feature Selection Based on the SVM Weight Vector for Classification of Dementia. IEEE J Biomed Heal Inform 19(5):1617–1626. https://doi.org/10.1109/JBHI.2015.2432832

    Article  Google Scholar 

  49. Tong T, Wolz R, Gao Q, Guerrero R, Hajnal JV, Rueckert D (2014) Multiple instance learning for classification of dementia in brain MRI. Med Image Anal 18(5):808–818. https://doi.org/10.1016/j.media.2014.04.006

    Article  Google Scholar 

  50. Gerardin E et al (2009) Multidimensional classification of hippocampal shape features discriminates Alzheimer’s disease and mild cognitive impairment from normal aging. Neuroimage 47(4):1476–1486. https://doi.org/10.1016/j.neuroimage.2009.05.036

    Article  Google Scholar 

  51. Zhang Y et al. (2015) Detection of subjects and brain regions related to Alzheimer’s disease using 3D MRI scans based on eigenbrain and machine learning, Front Comput Neurosci, vol. 9, no. June, pp. 1–15, doi: 10.3389/fncom.2015.00066

  52. Zhang Y, Wang S, Phillips P, Dong Z, Ji G, Yang J (2015) Detection of Alzheimer’s disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA-KSVM trained by PSOTVAC. Biomed Signal Process Control 21:58–73. https://doi.org/10.1016/j.bspc.2015.05.014

    Article  Google Scholar 

  53. Zhang Y, Wang S (2015) Detection of Alzheimer’s disease by displacement field and machine learning. PeerJ 2015(9):1–29. https://doi.org/10.7717/peerj.1251

    Article  Google Scholar 

  54. Xu Y, Pan X, Zhou Z, Yang Z, Zhang Y (2015) Structural least square twin support vector machine for classification. Appl Intell 42(3):527–536. https://doi.org/10.1007/s10489-014-0611-4

    Article  Google Scholar 

  55. Huang C, Yan B, Jiang H, Wang D (2008) Combining voxel-based morphometry with Artifical Neural Network theory in the application research of diagnosing Alzheimer’s disease, Biomed. Eng. Informatics New Dev. Futur. - Proc. 1st Int. Conf. Biomed. Eng. Informatics, BMEI 2008, vol. 1, pp. 250–254, doi: 10.1109/BMEI.2008.245

  56. Sankari Z, Adeli H (2011) Probabilistic neural networks for diagnosis of Alzheimer’s disease using conventional and wavelet coherence. J Neurosci Methods 197(1):165–170. https://doi.org/10.1016/j.jneumeth.2011.01.027

    Article  Google Scholar 

  57. Il Suk H, Shen D (2013) Deep learning-based feature representation for AD/MCI classification, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 8150 LNCS, no. PART 2, pp. 583–590, doi: 10.1007/978-3-642-40763-5_72

  58. Jain R, Jain N, Aggarwal A, Hemanth DJ (2019) Convolutional neural network based Alzheimer’s disease classification from magnetic resonance brain images. Cogn Syst Res 57:147–159. https://doi.org/10.1016/j.cogsys.2018.12.015

    Article  Google Scholar 

  59. Chaves R et al (2009) SVM-based computer-aided diagnosis of the Alzheimer’s disease using t-test NMSE feature selection with feature correlation weighting. Neurosci Lett 461(3):293–297. https://doi.org/10.1016/j.neulet.2009.06.052

    Article  Google Scholar 

  60. Plant C et al (2010) Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer’s disease. Neuroimage 50(1):162–174. https://doi.org/10.1016/j.neuroimage.2009.11.046

    Article  Google Scholar 

  61. Segovia F et al (2010) Classification of functional brain images using a GMM-based multi-variate approach. Neurosci Lett 474(1):58–62. https://doi.org/10.1016/j.neulet.2010.03.010

    Article  Google Scholar 

  62. Padilla P et al (2010) Analysis of SPECT brain images for the diagnosis of Alzheimer’s disease based on NMF for feature extraction. Neurosci Lett 479(3):192–196. https://doi.org/10.1016/j.neulet.2010.05.047

    Article  Google Scholar 

  63. Cuingnet R et al (2011) Automatic classification of patients with Alzheimer’s disease from structural MRI: A comparison of ten methods using the ADNI database. Neuroimage 56(2):766–781. https://doi.org/10.1016/j.neuroimage.2010.06.013

    Article  Google Scholar 

  64. Doecke JD et al (2012) Blood-based protein biomarkers for diagnosis of Alzheimer disease. Arch Neurol 69(10):1318–1325. https://doi.org/10.1001/archneurol.2012.1282

    Article  Google Scholar 

  65. Dukart J, Mueller K, Barthel H, Villringer A, Sabri O, Schroeter ML (2013) Meta-analysis based SVM classification enables accurate detection of Alzheimer’s disease across different clinical centers using FDG-PET and MRI. Psychiatry Res Neuroimaging 212(3):230–236. https://doi.org/10.1016/j.pscychresns.2012.04.007

    Article  Google Scholar 

  66. Ortiz A, Górriz JM, Ramírez J, Martínez-Murcia FJ (2013) LVQ-SVM based CAD tool applied to structural MRI for the diagnosis of the Alzheimer’s disease. Pattern Recogn Lett 34(14):1725–1733. https://doi.org/10.1016/j.patrec.2013.04.014

    Article  Google Scholar 

  67. Dti WM (2013) Read-2012Individual Classification of Mild Cognitive Impairment subtypes by support vector machine analysis of wihite matter DTI.pdf, pp. 283–291

  68. Lee W, Park B, Han K (2013) Classification of diffusion tensor images for the early detection of Alzheimer’s disease. Comput Biol Med 43(10):1313–1320. https://doi.org/10.1016/j.compbiomed.2013.07.004

    Article  Google Scholar 

  69. Hidalgo-Muñoz AR, Ramírez J, Górriz JM, Padilla P (2014) Regions of interest computed by SVM wrapped method for Alzheimer’s disease examination from segmented MRI, Front Aging Neurosci, vol. 6, no. FEB, pp. 1–10, doi: 10.3389/fnagi.2014.00020

  70. Lahmiri S, Boukadoum M (2014) New approach for automatic classification of Alzheimer’s disease, mild cognitive impairment and healthy brain magnetic resonance images. Healthc Technol Lett 1(1):32–36. https://doi.org/10.1049/htl.2013.0022

    Article  Google Scholar 

  71. Khedher L, Illán IA, Górriz JM, Ramírez J, Brahim A, Meyer-Baese A (2017) Independent Component Analysis-Support Vector Machine-Based Computer-Aided Diagnosis System for Alzheimer’s with Visual Support. Int J Neural Syst 27(3):1–18. https://doi.org/10.1142/S0129065716500507

    Article  Google Scholar 

  72. Ortiz A, Munilla J, Álvarez-Illán I, Górriz JM, Ramírez J (2015) Exploratory graphical models of functional and structural connectivity patterns for Alzheimer’s disease diagnosis, Front Comput Neurosci, vol. 9, no. November, pp. 1–18, doi: 10.3389/fncom.2015.00132

  73. Retico A, Bosco P, Cerello P, Fiorina E, Chincarini A, Fantacci ME (2015) Predictive Models Based on Support Vector Machines: Whole-Brain versus Regional Analysis of Structural MRI in the Alzheimer’s Disease. J Neuroimaging 25(4):552–563. https://doi.org/10.1111/jon.12163

    Article  Google Scholar 

  74. Havaei M, Guizard N, Chapados N, Bengio Y (2011) Medical Image Computing and Computer-Assisted Intervention , MICCAI 2011 14th Int. Conf. (Vision, Pattern Recognition, Graph., p. 697, doi: 10.1007/978-3-319-46720-7

  75. Lu S, Xia Y, Cai W, Fulham M, Feng DD (2017) Early identification of mild cognitive impairment using incomplete random forest-robust support vector machine and FDG-PET imaging. Comput Med Imaging Graph 60:35–41. https://doi.org/10.1016/j.compmedimag.2017.01.001

    Article  Google Scholar 

  76. Alam S, Kwon GR, Kim JI, Park CS (2017) Twin SVM-Based Classification of Alzheimer’s Disease Using Complex Dual-Tree Wavelet Principal Coefficients and LDA, J Healthc Eng, vol. 2017, doi: https://doi.org/10.1155/2017/8750506

  77. 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 282:69–80. https://doi.org/10.1016/j.jneumeth.2017.03.006

    Article  Google Scholar 

  78. Zeng N, Qiu H, Wang Z, Liu W, Zhang H, Li Y (2018) A new switching-delayed-PSO-based optimized SVM algorithm for diagnosis of Alzheimer’s disease. Neurocomputing 320:195–202. https://doi.org/10.1016/j.neucom.2018.09.001

    Article  Google Scholar 

  79. Lahmiri S, Shmuel A (2019) Performance of machine learning methods applied to structural MRI and ADAS cognitive scores in diagnosing Alzheimer’s disease. Biomed Signal Process Control 52:414–419. https://doi.org/10.1016/j.bspc.2018.08.009

    Article  Google Scholar 

  80. Kamathe RS, Joshi KR (2018) A novel method based on independent component analysis for brain MR image tissue classification into CSF, WM and GM for atrophy detection in Alzheimer’s disease. Biomed Signal Process Control 40:41–48. https://doi.org/10.1016/j.bspc.2017.09.005

    Article  Google Scholar 

  81. Peng J, Zhu X, Wang Y, An L, Shen D (2019) Structured sparsity regularized multiple kernel learning for Alzheimer’s disease diagnosis. Pattern Recogn 88:370–382. https://doi.org/10.1016/j.patcog.2018.11.027

    Article  Google Scholar 

  82. Sheng J et al. (2019) A novel joint HCPMMP method for automatically classifying Alzheimer’s and different stage MCI patients, Behav Brain Res, vol. 365, no. Mci, pp. 210–221, doi: 10.1016/j.bbr.2019.03.004

  83. Wang S (2015) Feed-forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection. Int J Imaging Syst Technol 25(2):153–164. https://doi.org/10.1002/ima.22132

    Article  MathSciNet  Google Scholar 

  84. Lama RK, Gwak J, Park JS, Lee SW (2017) Diagnosis of Alzheimer’s Disease Based on Structural MRI Images Using a Regularized Extreme Learning Machine and PCA Features. J Healthc Eng 1:2017. https://doi.org/10.1155/2017/5485080

    Article  Google Scholar 

  85. Jha D, Kim JI, Kwon GR (2017) Diagnosis of Alzheimer’s Disease Using Dual-Tree Complex Wavelet Transform, PCA, and Feed-Forward Neural Network, J. Healthc. Eng., vol. 2017, doi: https://doi.org/10.1155/2017/9060124

  86. Cheng B, Liu M, Il Suk H, Shen D, Zhang D (2015) Multimodal manifold-regularized transfer learning for MCI conversion prediction. Brain Imag Behav 9(4):913–926. https://doi.org/10.1007/s11682-015-9356-x

    Article  Google Scholar 

  87. Liu J, Li M, Lan W, Wu FX, Pan Y, Wang J (2018) Classification of Alzheimer’s Disease Using Whole Brain Hierarchical Network. IEEE/ACM Trans Comput Biol Bioinforma 15(2):624–632. https://doi.org/10.1109/TCBB.2016.2635144

    Article  Google Scholar 

  88. Kim J, Lee B (2018) Identification of Alzheimer’s disease and mild cognitive impairment using multimodal sparse hierarchical extreme learning machine. Hum Brain Mapp 39(9):3728–3741. https://doi.org/10.1002/hbm.24207

    Article  Google Scholar 

  89. Bi XA, Jiang Q, Sun Q, Shu Q, Liu Y (2018) Analysis of Alzheimer’s Disease Based on the Random Neural Network Cluster in fMRI, Front Neuroinform, vol. 12, no. September, pp. 1–10, doi: 10.3389/fninf.2018.00060

  90. Li W, Zhao Y, Chen X, Xiao Y, Qin Y (2019) Detecting Alzheimer’s Disease on Small Dataset: A Knowledge Transfer Perspective. IEEE J Biomed Heal Inform 23(3):1234–1242. https://doi.org/10.1109/JBHI.2018.2839771

    Article  Google Scholar 

  91. Mahmood R, Ghimire B (2013) Automatic detection and classification of Alzheimer’s disease from MRI scans using principal component analysis and artificial neural networks, Int Conf Syst Signals, Image Process., pp. 133–137, doi: 10.1109/IWSSIP.2013.6623471

  92. López M et al (2009) Automatic tool for Alzheimer’s disease diagnosis using PCA and Bayesian classification rules. Electron Lett 45(8):389–391. https://doi.org/10.1049/el.2009.0176

    Article  Google Scholar 

  93. Ramírez J et al (2010) Computer aided diagnosis system for the Alzheimer’s disease based on partial least squares and random forest SPECT image classification. Neurosci Lett 472(2):99–103. https://doi.org/10.1016/j.neulet.2010.01.056

    Article  Google Scholar 

  94. Chincarini A et al (2011) Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer’s disease. Neuroimage 58(2):469–480. https://doi.org/10.1016/j.neuroimage.2011.05.083

    Article  Google Scholar 

  95. Islam J, Zhang Y (2018) Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks, Brain Informatics, vol. 5, no. 2, doi: https://doi.org/10.1186/s40708-018-0080-3

  96. Spulber G et al (2013) An MRI-based index to measure the severity of Alzheimer’s disease-like structural pattern in subjects with mild cognitive impairment. J Intern Med 273(4):396–409. https://doi.org/10.1111/joim.12028

    Article  Google Scholar 

  97. Gray KR, Aljabar P, Heckemann RA, Hammers A, Rueckert D (2013) Random forest-based similarity measures for multi-modal classification of Alzheimer’s disease. Neuroimage 65:167–175. https://doi.org/10.1016/j.neuroimage.2012.09.065

    Article  Google Scholar 

  98. Chen Y, Pham TD (2013) Development of a brain MRI-based hidden Markov model for dementia recognition. Biomed Eng Online 12(SUPPL 1):1–16. https://doi.org/10.1186/1475-925X-12-S1-S2

    Article  Google Scholar 

  99. Li H, Liu Y, Gong P, Zhang C, Ye J (2014) Hierarchical interactions model for predicting Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD) conversion. PLoS One 9(1):1–11. https://doi.org/10.1371/journal.pone.0082450

    Article  Google Scholar 

  100. Liu S et al (2015) Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer’s Disease. IEEE Trans Biomed Eng 62(4):1132–1140. https://doi.org/10.1109/TBME.2014.2372011

    Article  Google Scholar 

  101. Il Suk H, Wee CY, Lee SW, Shen D (2016) State-space model with deep learning for functional dynamics estimation in resting-state fMRI. Neuroimage 129:292–307. https://doi.org/10.1016/j.neuroimage.2016.01.005

    Article  Google Scholar 

  102. Asgari M, Kaye J, Dodge H (2017) Predicting mild cognitive impairment from spontaneous spoken utterances. Alzheimer’s Dement Transl Res Clin Interv 3(2):219–228. https://doi.org/10.1016/j.trci.2017.01.006

    Article  Google Scholar 

  103. 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’s disease. Med Image Anal 46:26–34. https://doi.org/10.1016/j.media.2018.02.002

    Article  Google Scholar 

  104. Vaithinathan K, Parthiban L (2019) A Novel Texture Extraction Technique with T1 Weighted MRI for the Classification of Alzheimer’s Disease. J Neurosci Methods 318:84–99. https://doi.org/10.1016/j.jneumeth.2019.01.011

    Article  Google Scholar 

  105. Casini L, Roccetti M (2020) Medical Imaging and Artificial Intelligence. In: Lalumera, E., Fanti, S. (eds) Philosophy of Advanced Medical Imaging. SpringerBriefs in Ethics. Springer, Cham, pp. 81-95, doi: 10.1007/978-3-030-61412-6_7

  106. Ong KT (2018) ‘Challenges in Dementia Studies’, Alzheimer’s Disease - The 21st Century Challenge. InTech, Jul. 18, doi: 10.5772/intechopen.72866

  107. Martin SA, Townend FJ, Barkhof F, Cole JH (2023) Interpretable Machine Learning for Dementia: A Systematic Review. Alzheimer's & Dementia, vol. 19, Issue 5, pp. 2135-2149, doi: 10.1002/alz.12948

  108. Javeed A et al. (2023) Machine learning for dementia prediction: A systematic review and Future Research Directions, J Med Syst, vol. 47, no. 1, doi:https://doi.org/10.1007/s10916-023-01906-7

  109. Fenton L et al (2022) Cognitive and neuroimaging correlates of financial exploitation vulnerability in older adults without dementia: Implications for early detection of Alzheimer’s disease. Neurosci & Biobehav Rev 140:104773. https://doi.org/10.1016/j.neubiorev.2022.104773

    Article  Google Scholar 

  110. Wu C-C, Su C-H, Islam MM, Liao M-H (2023) Artificial Intelligence in dementia: A bibliometric study. Diagnostics 13(12):2109. https://doi.org/10.3390/diagnostics13122109

    Article  Google Scholar 

  111. Ford E, Milne R, Curlewis K (2023) Ethical issues when using digital biomarkers and artificial intelligence for the early detection of dementia, WIREs Data Mining and Knowledge Discovery, vol. 13, no. 3, doi:https://doi.org/10.1002/widm.1492

  112. Hashmi A, Barukab O (2023) Dementia classification using Deep Reinforcement Learning for early diagnosis. Appl Sci 13(3):1464. https://doi.org/10.3390/app13031464

    Article  Google Scholar 

  113. Https://www.alz.org/alzheimers-dementia/s, Stages of Alzheimer’s

  114. Sharma R, Goel T, Tanveer M, Murugan R (2022) FDN-ADNet: Fuzzy LS-TWSVM based deep learning network for prognosis of the Alzheimer’s disease using the sagittal plane of MRI scans. Appl Soft Comput 115:108099. https://doi.org/10.1016/j.asoc.2021.108099

    Article  Google Scholar 

  115. Ashraf A, Naz S, Shirazi SH, Razzak I, Parsad M (Jan. 2021) Deep transfer learning for alzheimer neurological disorder detection. Multimed Tools Appl 2021:1–26. https://doi.org/10.1007/S11042-020-10331-8

    Article  Google Scholar 

  116. Shanmugam JV, Duraisamy B, Simon BC, Bhaskaran P (2022) Alzheimer’s disease classification using pre-trained deep networks. Biomed Signal Process Control 71:103217. https://doi.org/10.1016/j.bspc.2021.103217

    Article  Google Scholar 

  117. Goenka N, Tiwari S (2022) AlzVNet: A volumetric convolutional neural network for multiclass classification of Alzheimer’s disease through multiple neuroimaging computational approaches. Biomed Signal Process Control 74:103500. https://doi.org/10.1016/j.bspc.2022.103500

    Article  Google Scholar 

  118. Leming M, Das S, Im H (2022) Construction of a confounder-free clinical MRI dataset in the Mass General Brigham system for classification of Alzheimer’s disease. Artif Intell Med 129:102309. https://doi.org/10.1016/j.artmed.2022.102309

    Article  Google Scholar 

  119. Liang Z, Zhang L (2022) Intuitionistic fuzzy twin support vector machines with the insensitive pinball loss. Appl Soft Comput 115:108231. https://doi.org/10.1016/j.asoc.2021.108231

    Article  Google Scholar 

Download references

Acknowledgements

I am thankful to Dr. Dilip Kumar, my supervisor and co-author of the manuscript form National Institute of Technology Jamshedpur Jharkhand, India, for his valuable time for continuous encouragement in writing this article

Author information

Authors and Affiliations

  1. NIT Jamshedpur, Jamshedpur, Jharkhand, 831014, India

    Nikhil Pateria & Dilip Kumar

Authors
  1. Nikhil Pateria
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dilip Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization- Nikhil Pateria; Writing original draft - Nikhil Pateria; Review and editing – Dilip Kumar.

Corresponding author

Correspondence to Nikhil Pateria.

Ethics declarations

Conflicts of interests

Also there is no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pateria, N., Kumar, D. A comprehensive review on detection and classification of dementia using neuroimaging and machine learning. Multimed Tools Appl 83, 52365–52403 (2024). https://doi.org/10.1007/s11042-023-17288-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-17288-4

Keywords

Search

Navigation