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Volume 25, Issue 4
  • ISSN: 1568-0266
  • E-ISSN: 1873-4294

Abstract

Alzheimer's Disease (AD) is a serious neurological illness that causes memory loss gradually by destroying brain cells. This deadly brain illness primarily strikes the elderly, impairing their cognitive and bodily abilities until brain shrinkage occurs. Modern techniques are required for an accurate diagnosis of AD. Machine learning has gained attraction in the medical field as a means of determining a person's risk of developing AD in its early stages. One of the most advanced soft computing neural network-based Deep Learning (DL) methodologies has garnered significant interest among researchers in automating early-stage AD diagnosis. Hence, a comprehensive review is necessary to gain insights into DL techniques for the advancement of more effective methods for diagnosing AD.

This review explores multiple biomarkers associated with Alzheimer's Disease (AD) and various DL methodologies, including Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), The k-nearest-neighbor (k-NN), Deep Boltzmann Machines (DBM), and Deep Belief Networks (DBN), which have been employed for automating the early diagnosis of AD. Moreover, the unique contributions of this review include the classification of ATN biomarkers for Alzheimer's Disease (AD), systemic description of diverse DL algorithms for early AD assessment, along with a discussion of widely utilized online datasets such as ADNI, OASIS, Additionally, this review provides perspectives on future trends derived from critical evaluation of each variant of DL techniques across different modalities, dataset sources, AUC values, and accuracies.

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References

  1. (a) GerberA.; Becker, A.; Jung, J.; Strathmann, F.; Lauber, C. The histological examination of exceptionally well-preserved brain tissues from F. Johan, marking the second reported case of Alzheimer's disease.J. Neurobiol.199952123130 (b) Keuck, L. History as a biomedical matter: Recent reassessments of the first cases of Alzheimer’s disease. Hist. Philos. Life Sci., 2018, 40(1), 10. 10.1007/s40656‑017‑0177‑729181598
     [Google Scholar]
  2. TuanT.A. PhamT.B. KimJ.Y. TavaresJ.M.R.S. Alzheimer’s diagnosis using deep learning in segmenting and classifying 3D brain MR images.Int. J. Neurosci.2022132768969810.1080/00207454.2020.183590033045895
     [Google Scholar]
  3. DyrbaM. EwersM. WegrzynM. KilimannI. PlantC. OswaldA. MeindlT. PievaniM. BokdeA.L.W. FellgiebelA. FilippiM. HampelH. KlöppelS. HauensteinK. KirsteT. TeipelS.J. Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data.PLoS One201385e6492510.1371/journal.pone.006492523741425
     [Google Scholar]
  4. FanZ. XuF. QiX. LiC. YaoL. Classification of Alzheimer’s disease based on brain MRI and machine learning.Neural Comput. Appl.20203271927193610.1007/s00521‑019‑04495‑0
     [Google Scholar]
  5. KogaS. IkedaA. DicksonD.W. Deep learning-based model for diagnosing Alzheimer’s disease and tauopathies.Neuropathol. Appl. Neurobiol.2022481e1275910.1111/nan.1275934402107
     [Google Scholar]
  6. FinneyC.A. DelerueF. GoldW.A. BrownD.A. ShvetcovA. Artificial intelligence-driven meta-analysis of brain gene expression identifies novel gene candidates and a role for mitochondria in Alzheimer’s disease.Comput. Struct. Biotechnol. J.20232138840010.1016/j.csbj.2022.12.01836618979
     [Google Scholar]
  7. Alzheimer’s disease facts and figures.Alzheimers Dement.202117332740610.1002/alz.1232833756057
     [Google Scholar]
  8. KlöppelS. StonningtonC.M. ChuC. DraganskiB. ScahillR.I. RohrerJ.D. FoxN.C. JackC.R.Jr AshburnerJ. FrackowiakR.S.J. Automatic classification of MR scans in Alzheimer’s disease.Brain2008131368168910.1093/brain/awm31918202106
     [Google Scholar]
  9. MoradiE. PepeA. GaserC. HuttunenH. TohkaJ. Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects.Neuroimage201510439841210.1016/j.neuroimage.2014.10.00225312773
     [Google Scholar]
  10. McKhannG.M. KnopmanD.S. ChertkowH. HymanB.T. JackC.R.Jr KawasC.H. KlunkW.E. KoroshetzW.J. ManlyJ.J. MayeuxR. MohsR.C. MorrisJ.C. RossorM.N. ScheltensP. CarrilloM.C. ThiesB. WeintraubS. PhelpsC.H. The Diagnosis of Dementia Due to Alzheimer’s Disease: Recommendations From the National Institute on Aging-Alzheimer’s Association Workgroups on Diagnostic Guidelines for Alzheimer’s Disease.Alzheimers Dement.20117263269
     [Google Scholar]
  11. CarrilloM.C. DeanR.A. NicolasF. MillerD.S. BermanR. KhachaturianZ. BainL.J. SchindlerR. KnopmanD. Revisiting the framework of the National Institute on Aging-Alzheimer’s Association diagnostic criteria.Alzheimers Dement.20139559460110.1016/j.jalz.2013.05.176224007744
     [Google Scholar]
  12. KhanA. ZubairS. An Improved Multi-Modal Based Machine Learning Approach for the Prognosis of Alzheimer’s Disease.J. King Saud Univ. Comput. Inf. Sci.20223426882706
     [Google Scholar]
  13. BeggR. LaiD.T.H. PalaniswamiM. Computational Intelligence in Biomedical Engineering.1st edCRC Press200710.1201/9781420005899
     [Google Scholar]
  14. de BruijneM. Machine learning approaches in medical image analysis: From detection to diagnosis.Med. Image Anal.201633949710.1016/j.media.2016.06.03227481324
     [Google Scholar]
  15. BeheshtiI. DemirelH. FarokhianF. YangC. MatsudaH. Structural MRI-based detection of Alzheimer’s disease using feature ranking and classification error.Comput. Methods Programs Biomed.201613717719310.1016/j.cmpb.2016.09.01928110723
     [Google Scholar]
  16. CabralC. MorgadoP.M. CostaC.D. SilveiraM. Predicting conversion from MCI to AD with FDG-PET brain images at different prodromal stages.Comput. Biol. Med.20155810110910.1016/j.compbiomed.2015.01.00325625698
     [Google Scholar]
  17. ShahH. PatelA. ParikhV. NaganiA. BhimaniB. ShahU. BambharoliyaT. The β- secretase enzyme BACE1: A biochemical enigma for Alzheimer’s disease.CNS Neurol. Disord. Drug Targets202019318419410.2174/187152731966620052614414132452328
     [Google Scholar]
  18. WasonR. Deep learning: Evolution and expansion.Cogn. Syst. Res.20185270170810.1016/j.cogsys.2018.08.023
     [Google Scholar]
  19. CireşanD.C. GiustiA. GambardellaL.M. SchmidhuberJ. Mitosis detection in breast cancer histology images with deep neural networks.International Conf. on Med. Image Computing and Computer-assisted InterventionSpringerHeidelberg201341141810.1007/978‑3‑642‑40763‑5_51
     [Google Scholar]
  20. RameshS. CaytilesR.D. IyengaN.C.S.N. CaytilesR.D. IyengarN.C.S.N. A deep-learning approach to identify diabetes.Adv. Sci. Tech. Lett.2017145444910.14257/astl.2017.145.09
     [Google Scholar]
  21. MaX. YangH. ChenQ. HuangD. WangY. Depaudionet: An efficient deep model for audio-based depression classification.Proceed. 6th Int. Workshop Audio/Visual Emot. Chall.ACM2016354210.1145/2988257.2988267
     [Google Scholar]
  22. HeinsfeldA.S. FrancoA.R. CraddockR.C. BuchweitzA. MeneguzziF. Identification of autism spectrum disorder using deep learning and the ABIDE dataset.Neuroimage Clin.201817162310.1016/j.nicl.2017.08.01729034163
     [Google Scholar]
  23. DaoudH.G. AbdelhameedA.M. Automatic epileptic seizure detection based on empirical mode decomposition and deep neural network.IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA)09-10 MarchPenang, Malaysia2018182186
     [Google Scholar]
  24. KadamV.J. JadhavS. VijayakumarK. Feature Ensemble Learning Based on Sparse Auto-encoders for Diagnosis of Parkinson’s Disease.Adv. Intell. Syst. Comp.SpringerSingapore2019810567581
     [Google Scholar]
  25. GulhareK.K. ShuklaS.P. SharmaL.K. Deep Neural Network Classification method to Alzheimer?s Disease Detection.Int. J. Adv. Res. Comput. Sci. Softw. Eng.2017761410.23956/ijarcsse/V7I6/0259
     [Google Scholar]
  26. ZaherA.A.M. EldeibA.M. Breast cancer classification using deep belief networks.Expert Syst. Appl.20164613914410.1016/j.eswa.2015.10.015
     [Google Scholar]
  27. KimJ. KangU. LeeY. Statistics and deep belief network-based cardiovascular risk prediction.Healthc. Inform. Res.201723316917510.4258/hir.2017.23.3.16928875051
     [Google Scholar]
  28. CaiH. ShaX. HanX. WeiS. HuB. Pervasive EEG diagnosis of depression using Deep Belief Network with three-electrodes EEG collector.IEEE International Conference on Bioinformatics and Biomedicine15-18 DecemberShenzhen201612391246
     [Google Scholar]
  29. XuJ. XiangL. LiuQ. GilmoreH. WuJ. TangJ. MadabhushiA. Stacked sparse autoencoder for nuclei detection on breast cancer-histopathology images.IEEE Trans. Med. Imaging201635111913010.1109/TMI.2015.245870226208307
     [Google Scholar]
  30. ChenL. ZhouM. SuW. WuM. SheJ. HirotaK. Softmax regression based deep sparse autoencoder network for facial emotion recognition in human-robot interaction.Inf. Sci.2018428496110.1016/j.ins.2017.10.044
     [Google Scholar]
  31. DolphC.V. AlamM. ShboulZ. SamadM.D. IftekharuddinK.M. Deep learning of texture and structural features for multiclass Alzheimer’s disease classification.International Joint Conference on Neural Networks14-19 MayAnchorage, AK, USA20172259226610.1109/IJCNN.2017.7966129
     [Google Scholar]
  32. ZhangY.N. Can a smartphone diagnose Parkinson disease? A deep neural network method and telediagnosis system implementation.Parkinsons Dis.2017201711110.1155/2017/620970329075547
     [Google Scholar]
  33. ChoJ. HuZ. SartipiM. Post-stroke discharge disposition prediction using deep learning.SoutheastConConcord, NC, USA30 March - 02 April20171210.1109/SECON.2017.7925299
     [Google Scholar]
  34. LinQ. YeS.Q. HuangX.M. LiS.Y. ZhangM.Z. XueY. ChenW.S. Classification of epileptic EEG signals with stacked sparse autoencoder based on deep learning.Intel. Comp. Methodol.SpringerCham2016977380281010.1007/978‑3‑319‑42297‑8_74
     [Google Scholar]
  35. WuL. ZhouW. WanX. ZhangJ. ShenL. HuS. DingQ. MuG. YinA. HuangX. LiuJ. JiangX. WangZ. DengY. LiuM. LinR. LingT. LiP. WuQ. JinP. ChenJ. YuH. A deep neural network improves endoscopic detection of early gastric cancer without blind spots.Endoscopy201951652253110.1055/a‑0855‑353230861533
     [Google Scholar]
  36. AlbaharM.A. Skin lesion classification using convolutional neural network with novel Regularizer.IEEE Access20197383063831310.1109/ACCESS.2019.2906241
     [Google Scholar]
  37. RenX. XiangL. NieD. ShaoY. ZhangH. ShenD. WangQ. Interleaved 3D- CNN s for joint segmentation of small-volume structures in head and neck CT images.Med. Phys.20184552063207510.1002/mp.1283729480928
     [Google Scholar]
  38. LiH. WangY. KinnardL. FreedmanM.T. FreedmanM.T. A multiple circular path convolution neural network system for detection of mammographic masses.IEEE Trans. Med. Imaging200221215015810.1109/42.99313311929102
     [Google Scholar]
  39. LangR. ZhaoL. JiaK. Brain tumour image segmentation based on convolution neural network.BioMed. Engg. Inform.IEEE201614021406
     [Google Scholar]
  40. DhamS. SharmaA. DhallA. Depression scale recognition from audio, visual and text analysis.arXiv2017170905865
     [Google Scholar]
  41. WanS. LiangY. ZhangY. GuizaniM. Deep multi-layer perceptron classifier for behavior analysis to estimate Parkinson’s disease severity using smartphones.IEEE Access20186368253683310.1109/ACCESS.2018.2851382
     [Google Scholar]
  42. YangH. ZhangJ. LiuQ. WangY. Multimodal MRI-based classification of migraine: Using deep learning CNN.Biomed. Eng. Online201817113810.1186/s12938‑018‑0587‑030314437
     [Google Scholar]
  43. LiG. LiuM. SunQ. ShenD. WangL. Early diagnosis of autism disease by multi-channel CNNs.Mach Learn Med Imaging.20181104630330910.1007/978‑3‑030‑00919‑9_35
     [Google Scholar]
  44. AwateG. BangareS. PradeepiniG. PatilS. Detection of Alzheimer’s Disease from MRI using Convolutional Neural Network with TensorFlow.arXiv2018180610170
     [Google Scholar]
  45. GongJ. LachJ. QiY. GoldmanM.D. Causal analysis of inertial body sensors for enhancing gait assessment separability towards multiple sclerosis diagnosis.IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks09-12 JuneCambridge, MA, USA20151610.1109/BSN.2015.7299400
     [Google Scholar]
  46. MaJ. WuF. ZhuJ. XuD. KongD. A pre-trained CNN-based method for thyroid nodule diagnosis.Ultrasonics20177322123010.1016/j.ultras.2016.09.01127668999
     [Google Scholar]
  47. ÖmanO. MäkeläT. SalliE. SavolainenS. KangasniemiM. 3D convolutional neural networks applied to CT angiography in the detection of acute ischemic stroke.Eur. Radiol. Exp.201931810.1186/s41747‑019‑0085‑630758694
     [Google Scholar]
  48. JackC.R.Jr BennettD.A. BlennowK. CarrilloM.C. FeldmanH.H. FrisoniG.B. HampelH. JagustW.J. JohnsonK.A. KnopmanD.S. PetersenR.C. ScheltensP. SperlingR.A. DuboisB. RankinK.P. RoweC.C. ScheltensP. SiemersE. SnyderH.M. SperlingR. A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers.Neurology201687553954710.1212/WNL.000000000000292327371494
     [Google Scholar]
  49. JackC.R.Jr BennettD.A. BlennowK. CarrilloM.C. DunnB. HaeberleinS.B. HoltzmanD.M. JagustW. JessenF. KarlawishJ. LiuE. MolinuevoJ.L. MontineT. PhelpsC. RankinK.P. RoweC.C. ScheltensP. SiemersE. SnyderH.M. SperlingR. ElliottC. MasliahE. RyanL. SilverbergN. NIA-AA Research Framework: Toward a biological definition of Alzheimer’s disease.Alzheimers Dement.201814453556210.1016/j.jalz.2018.02.01829653606
     [Google Scholar]
  50. DuboisB. HampelH. FeldmanH.H. ScheltensP. AisenP. AndrieuS. BakardjianH. BenaliH. BertramL. BlennowK. BroichK. CavedoE. CrutchS. DartiguesJ.F. DuyckaertsC. EpelbaumS. FrisoniG.B. GauthierS. GenthonR. GouwA.A. HabertM.O. HoltzmanD.M. KivipeltoM. ListaS. MolinuevoJ.L. O’BryantS.E. RabinoviciG.D. RoweC. SallowayS. SchneiderL.S. SperlingR. TeichmannM. CarrilloM.C. CummingsJ. JackC.R.Jr Preclinical Alzheimer’s disease: Definition, natural history, and diagnostic criteria.Alzheimers Dement.201612329232310.1016/j.jalz.2016.02.00227012484
     [Google Scholar]
  51. RahimiJ. KovacsG.G. Prevalence of mixed pathologies in the aging brain.Alzheimers Res. Ther.2014698210.1186/s13195‑014‑0082‑125419243
     [Google Scholar]
  52. RobinsonJ.L. LeeE.B. XieS.X. RennertL. SuhE. BredenbergC. CaswellC. Van DeerlinV.M. YanN. YousefA. HurtigH.I. SiderowfA. GrossmanM. McMillanC.T. MillerB. DudaJ.E. IrwinD.J. WolkD. ElmanL. McCluskeyL. PlotkinC.A. WeintraubD. ArnoldS.E. BrettschneiderJ. LeeV.M.Y. TrojanowskiJ.Q. Neurodegenerative disease concomitant proteinopathies are prevalent, age-related and APOE4-associated.Brain201814172181219310.1093/brain/awy14629878075
     [Google Scholar]
  53. GawehnE. HissJ.A. SchneiderG. Deep learning in drug discovery.Mol. Inform.201635131410.1002/minf.20150100827491648
     [Google Scholar]
  54. VieiraS. PinayaW.H.L. MechelliA. Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications.Neurosci. Biobehav. Rev.201774Pt A587510.1016/j.neubiorev.2017.01.00228087243
     [Google Scholar]
  55. NairM.D. SintaM. VidyaM. A study on various deep learning algorithms to diagnose Alzheimer’s disease.Proceed. Int. Conf. ISMAC Comp. Vision Bio-Eng.SpringerCham20183017051710
     [Google Scholar]
  56. LiF. TranL. ThungK.H. JiS. ShenD. LiJ. A robust deep model for improved classification of AD/MCI patients.IEEE J. Biomed. Health Inform.20151951610161610.1109/JBHI.2015.242955625955998
     [Google Scholar]
  57. (a) FengL.; Tran, L.; Thung, K.-H.; Ji, S.; Shen, D.; Li, J. A robust deep model for improved classification of AD/MCI patients.IEEE J. Biomed. Health Inform.201519516101616 (b) Hazarika, R.A.; Abraham, A.; Kandar, D.; Maji, A.K. An Improved LeNet-Deep Neural Network Model for Alzheimer’s Disease Classification Using Brain Magnetic Resonance Images. IEEE Access, 2021, 9, 161194-161207. http://dx.doi.org/10.1109/ACCESS.2021.3131741
     [Google Scholar]
  58. SoundaryaS. SruthiM.S. BamaS.S. KiruthikaS. DhiyaneswaranJ. Early Detection of Alzheimer’s Disease Using Gadolinium Material.Mater. Today Proc.20214521094110110.1016/j.matpr.2020.03.189
     [Google Scholar]
  59. LeeT. LeeH. Prediction of Alzheimer’s disease using blood gene expression data.Sci. Rep.2020101348510.1038/s41598‑020‑60595‑132103140
     [Google Scholar]
  60. AmorosoN. DiaconoD. FanizziA. La RoccaM. MonacoA. LombardiA. GuaragnellaC. BellottiR. TangaroS. Deep learning reveals Alzheimer’s disease onset in MCI subjects: Results from an international challenge.J. Neurosci. Methods20183023910.1016/j.jneumeth.2017.12.01129287745
     [Google Scholar]
  61. LecunY. BottouL. BengioY. HaffnerP. Gradient-based learning applied to document recognition.Proc. IEEE199886112278232410.1109/5.726791
     [Google Scholar]
  62. EickenbergM. GramfortA. VaroquauxG. ThirionB. Seeing it all: Convolutional network layers map the function of the human visual system.Neuroimage201715218419410.1016/j.neuroimage.2016.10.00127777172
     [Google Scholar]
  63. McCrackinL. Early detection of Alzheimer’s disease using deep learning.31st Canadian Conference on Artificial Intelligence, Canadian AIMay 8–11Toronto, ON, Canada201835535910.1007/978‑3‑319‑89656‑4_40
     [Google Scholar]
  64. AslH.E. GhazalM. MahmoudA. AslantasA. ShalabyA.M. CasanovaM.F. BarnesG.N. Gimel’farbG. KeyntonR. El-BazA. Alzheimer’s disease diagnostics by a 3D deeply supervised adaptable convolutional network.Front. Biosci.201823358459628930562
     [Google Scholar]
  65. LiF. LiuM. Alzheimer’s disease diagnosis based on multiple cluster dense convolutional networks.Comput. Med. Imaging Graph.20187010111010.1016/j.compmedimag.2018.09.00930340094
     [Google Scholar]
  66. KruthikaK.R. Rajeswari MaheshappaH.D. CBIR system using Capsule Networks and 3D CNN for Alzheimer’s disease diagnosis.Inform. Med. Unlocked201914596810.1016/j.imu.2018.12.001
     [Google Scholar]
  67. PouyanfarS. SadiqS. YanY. TianH. TaoY. ReyesM.P. ShyuM-L. ChenS-C. IyengarS.S. A survey on deep learning: Algorithms, techniques, and applications.ACM Comput. Surv.201951513610.1145/3234150
     [Google Scholar]
  68. DengL. A tutorial survey of architectures, algorithms, and applications for deep learning.APSIPA Trans. Signal. Inf. Process.20143112910.1017/atsip.2013.9
     [Google Scholar]
  69. NguyenM. HeT. AnL. AlexanderD.C. FengJ. YeoB.T.T. Predicting Alzheimer’s disease progression using deep recurrent neural networks.Neuroimage202022211720311721810.1016/j.neuroimage.2020.11720332763427
     [Google Scholar]
  70. CuiR. LiuM. RNN-based longitudinal analysis for diagnosis of Alzheimer’s disease.Comput. Med. Imaging Graph.20197311010.1016/j.compmedimag.2019.01.00530763637
     [Google Scholar]
  71. LiF. LiuM. A hybrid Convolutional and Recurrent Neural Network for Hippocampus Analysis in Alzheimer’s Disease.J. Neurosci. Methods201932310811810.1016/j.jneumeth.2019.05.00631132373
     [Google Scholar]
  72. WangT. QiuR.G. YuM. Predictive Modeling of the Progression of Alzheimer’s Disease with Recurrent Neural Networks.Sci. Rep.2018819161917310.1038/s41598‑018‑27337‑w29907747
     [Google Scholar]
  73. ChienY.W. HongS.Y. CheahW.T. YaoL.H. ChangY.L. FuL.C. An Automatic Assessment System for Alzheimer’s Disease Based on Speech Using Feature Sequence Generator and Recurrent Neural Network.Sci. Rep.201991195971960710.1038/s41598‑019‑56020‑x31862920
     [Google Scholar]
  74. HanJ. PeiJ. KamberM. Data Mining: Concepts and Techniques.4th edElsevier2011
     [Google Scholar]
  75. (a) KalbkhaniH.; Mirzaei, A.; Mohebbi, M. Robust algorithm for disease diagnosis utilizing MRI data: Combining 2D DWT, PCA, and LDA for feature extraction with k-NN and SVM classifiers.IEEE Trans. Med. Imaging202039824342443 (b) Wu, X.; Kumar, V.; Quinlan, R.J.; Ghosh, J.; Yang, Q.; Motoda, H.; McLachlan, G.J.; Ng, A.; Liu, B.; Yu, P.S.; Zhou, Z-H.; Steinbach, M.; Hand, D.J.; Steinberg, D. Top 10 algorithms in data mining. Knowl. Inf. Syst., 2008, 14(1), 1-37. http://dx.doi.org/10.1007/s10115-007-0114-2
     [Google Scholar]
  76. MahendranN. DuraiR. A deep belief network-based approach for detecting to identify proteomic risk markers for Alzheimer's disease using the multi-omics data.Comp. Struct. Biotechnol. J.20232116511660
     [Google Scholar]
  77. LopesN. RibeiroB. GonçalvesJ. Restricted Boltzmann Machines and Deep Belief Networks on multi-core processors.International Joint Conference on Neural Networks (IJCNN)10-15 JuneBrisbane, QLD, Australia20121710.1109/IJCNN.2012.6252431
     [Google Scholar]
  78. Le RouxN. BengioY. Representational power of restricted boltzmann machines and deep belief networks.Neural Comput.20082061631164910.1162/neco.2008.04‑07‑51018254699
     [Google Scholar]
  79. EbrahimighahnaviehM.A. LuoS. ChiongR. Deep learning to detect Alzheimer’s disease from neuroimaging: A systematic literature review.Comput. Methods Programs Biomed.202018710524210.1016/j.cmpb.2019.10524231837630
     [Google Scholar]
  80. ShenD. WuG. SukH.I. Deep learning in medical image analysis.Annu. Rev. Biomed. Eng.201719122124810.1146/annurev‑bioeng‑071516‑04444228301734
     [Google Scholar]
  81. SukH.I. LeeS.W. ShenD. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis.Neuroimage201410156958210.1016/j.neuroimage.2014.06.07725042445
     [Google Scholar]
  82. KamadaS. IchimuraT. HaradaT. Image-Based Early Detection of Alzheimer’s Disease by Using Adaptive Structural Deep Learning.Smart Innovation, Systems and Technologies.SingaporeSpringer2021595605
     [Google Scholar]
  83. GoelT. SharmaR. TanveerM. SuganthanP.N. MajiK. PilliR. Multimodal neuroimaging based alzheimer’s disease diagnosis using evolutionary rvfl classifier.IEEE J. Biomed. Health Inform.2023PP1937022418
     [Google Scholar]
  84. SharmaR. GoelT. TanveerM. LinC.T. MuruganR. Deep learning based diagnosis and prognosis of Alzheimer’s disease: A comprehensive review.IEEE Trans. Cogn. Dev. Syst.20231531123113810.1109/TCDS.2023.3254209
     [Google Scholar]
  85. BalakrishnanN.B. PSS. PanackalJ.J. Alzheimer’s Disease Diagnosis using Machine Learning: A Review.Int. J. Eng. Trends Technol.202271312012910.14445/22315381/IJETT‑V71I3P213
     [Google Scholar]
  86. VermaS. GoelT. TanveerM. DingW. SharmaR. MuruganR. Machine learning techniques for the Schizophrenia diagnosis: A comprehensive review and future research directions.J. Ambient Intell. Humaniz. Comput.20231454795480710.1007/s12652‑023‑04536‑6
     [Google Scholar]
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A Comprehensive Review on Deep Learning Techniques in Alzheimer’s Disease Diagnosis
Curr. Top. Med. Chem. 25, 335 (2025); https://doi.org/10.2174/0115680266310776240524061252
/content/journals/ctmc/10.2174/0115680266310776240524061252
/content/journals/ctmc/10.2174/0115680266310776240524061252
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  • Article Type:     Review Article
Keyword(s): Alzheimer's disease; Convolutional neural networks; Deep belief networks (DBN); Deep boltzmann machines; Deep learning; Recurrent neural networks

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