Abstract
Alzheimer’s disease (AD) is the most common cause of dementia worldwide; it is a progressive degenerative neurological disorder; due to it, the brain cells die slowly. Early detection of the disease is crucial for deploying interventions and slowing its progression. In the past decade, many machine learning and deep learning algorithms have been explored to build automated detection for Alzheimer’s. Advancements in data augmentation techniques and deep learning architectures have opened up new frontiers in this field, and research is moving rapidly. Hence, this survey aims to provide an overview of recent research on deep learning models for Alzheimer's disease diagnosis. In addition to categorizing the numerous data sources, neural network architectures, and commonly used assessment measures, we also classify implementation and reproducibility. Our objective is to assist interested researchers in keeping up with the newest developments and reproducing earlier investigations as benchmarks. In addition, we also indicate future research directions for this topic.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Dementia. https://www.who.int/news-room/fact-sheets/detail/dementia. Accessed 20 May 2022
Singh N, Soni N, Kapoor A (2022) Automated detection of Alzheimer disease using MRI images and deep neural networks—a review. Preprint at https://arxiv.org/pdf/2209.11282.pdf
Borchert RJ et al (2021) Artificial intelligence for diagnosis and prognosis in neuroimaging for dementia; a systematic review. medRxiv
Zhang L, Wang L, Zhu D (2020) Jointly Analyzing Alzheimer’s disease related structure-function using deep cross-model attention network. In: 2020 IEEE 17th international symposium on biomedical imaging (ISBI). IEEE, pp 563–567
Jack Jr CR, Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, et al(2008) The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J Magn Reson Imaging: Off J Int Soc Magn Res-Onance Med 27:685–691. https://doi.org/10.1002/jmri.21049
Marcus DS, Wang TH, Parker J, Csernansky JG, Morris JC, Buckner RL (2007) Open access series of imaging studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J Cogn Neurosci 19:1498–1507. https://doi.org/10.1162/jocn.2007.19.9.1498
Ramzan F, Khan MUG, Iqbal S, Saba T, Rehman A (2020) Volumetric segmentation of brain regions from MRI scans using 3D convolutional neural networks. IEEE Access 8:103697–103709
B. Landman S (2012) Warfield, MICCAI 2012 workshop on multi-atlas labeling. In: MICCAI grand challenge and workshop on multi-atlas labeling. CreateSpace Independent Publishing Platform, Nice, France
Goenka N, Tiwari S (2021) Volumetric convolutional neural network for Alzheimer detection. In: 5th International conference on trends in electronics and informatics (ICOEI). IEEE, pp 1500–1505
Malone IB, Cash D, Ridgway GR, Macmanus DG, Ourselin S, Fox NC, Schott JM (2012) MIRIAD-Public release of a multiple time point Alzheimer’s MR imaging dataset. Neuroimage 70C:33–36. https://doi.org/10.1016/j.neuroimage.2012.12.044
Salehi AW, Baglat P, Sharma BB, Gupta G, Upadhya AA (2020) CNN model: earlier diagnosis and classification of Alzheimer disease using MRI. In: International conference on smart electronics and communication (ICOSEC). IEEE, pp 156–161
Zubair L, Irtaza SA, Nida N, ul Haq N (2021) Alzheimer and mild cognitive disease recognition using automated deep learning techniques. In: International Bhurban conference on applied sciences and technologies (IBCAST). IEEE, pp 310–315
Ahmad MF, Akbar S, Hassan SAE, Rehman A, Ayesha N (2021) Deep learning approach to diagnose Alzheimer’s disease through magnetic resonance images. In: International conference on innovative computing (ICIC). IEEE, pp 1–6
Subaramya S, Kokul T, Nagulan R, Pinidiyaarachchi UAJ, Jeyasuthan M (2021) Detection of Alzheimer’s disease using structural brain network and convolutional neural network. In: 10th international conference on information and automation for sustainability (ICIAfS). IEEE, pp 173–178
Sadat SU, Shomee HH, Awwal A, Amin SN, Reza MT, Parvez MZ (2021) Alzheimer’s disease detection and classification using transfer learning technique and ensemble on convolutional neural networks. In: IEEE international conference on systems, man, and cybernetics (SMC). IEEE, pp 1478–1481
Murugan S, Venkatesan C, Sumithra MG, Gao XZ, Elakkiya B, Akila M, Manoharan S (2021) DEMNET: a deep learning model for early diagnosis of Alzheimer diseases and dementia from MR images. IEEE Access 9:90319–90329
Naz S, Ashraf A, Zaib A (2021) Transfer learning using freeze features for Alzheimer neurological disorder detection using ADNI dataset. Multimedia Syst 28(1):85–94
Janghel RR, Rathore YK (2021) Deep convolution neural network-based system for early diagnosis of Alzheimer’s disease. Irbm 42(4):258–267
Mehmood A, Yang S, Feng Z, Wang M, Ahmad AS, Khan R, Yaqub M et al (2021) A transfer learning approach for early diagnosis of Alzheimer’s disease on MRI images. Neuroscience 460:43–52
Rajeswari SS, Nair M (2021) A transfer learning approach for predicting Alzheimer’s disease. In: 4th Biennial international conference on nascent technologies in engineering (ICNTE). IEEE, pp 1–5
Fujibayashi D, Sakaguchi H, Ardakani I, Okuno A (2021) Nonlinear registration as an effective preprocessing technique for deep learning based classification of disease. In: 43rd annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 3245–3250
Puente-Castro A, Fernandez-Blanco E, Pazos A, Munteanu CR (2020) Automatic assessment of Alzheimer’s disease diagnosis based on deep learning techniques. Comput Biol Med 120:103764
Odusami M, Maskeliūnas R, Damaševičius R, Krilavičius T (2021) Analysis of features of Alzheimer’s disease: detection of early stage from functional brain changes in magnetic resonance images using a finetuned ResNet18 network. Diagnostics 11(6):1071
Buvaneswari PR, Gayathri R (2021) Deep learning-based segmentation in classification of Alzheimer’s disease. Arab J Sci Eng 46(6):5373–5383
Fiasam D, Linda R, Yunbo S, Collins A, Osei I, Mawuli CB (2022) Efficient 3D residual network on MRI data for neurodegenerative disease classification. Proc SPIE 12083:120831A-1
Bi X, Li S, Xiao B, Li Y, Wang G, Ma X (2020) Computer aided Alzheimer’s disease diagnosis by an unsupervised deep learning technology. Neurocomputing 392:296–304
Jia H, Wang Y, Duan Y, Xiao H (2021) Alzheimer’s disease classification based on image transformation and features fusion. Comput Math Methods Meds
Wang Y, Jia H, Duan Y, Xiao H (2021) Applying 3DPCANet and functional magnetic resonance imaging to aided diagnosis of Alzheimer’s disease. Res Sq preprint
Mohammed BA, Senan EM, Rassem TH, Makbol NM, Alanazi AA, Al-Mekhlafi ZG, Ghaleb FA et al (2021) Multi-method analysis of medical records and MRI images for early diagnosis of dementia and Alzheimer’s disease based on deep learning and hybrid methods. Electronics 10(22):2860
Bi X, Zhao X, Huang H, Chen D, Ma Y (2020) Functional brain network classification for Alzheimer’s disease detection with deep features and extreme learning machine. Cogn Comput 12(3):513–527
Wang Z, Xin J, Wang Z, Gu H, Zhao Y, Qian W (2021) Computer-aided dementia diagnosis based on hierarchical extreme learning machine. Cogn Comput 13(1):34–48
Saratxaga CL, Moya I, Picón A, Acosta M, Moreno-Fernandez-de-Leceta A, Garrote E, Bereciartua-Perez A (2021) MRI deep learning-based solution for Alzheimer’s disease prediction. J Pers Med 11(9):902
Ali TWD (2021) Deep convolutional second generation curvelet transform-based MR image for early detection of Alzheimer’s disease
Basheer S, Bhatia S, Sakri SB (2021) Computational modeling of dementia prediction using deep neural network: analysis on OASIS dataset. IEEE Access 9:42449–42462
Fan Z, Li J, Zhang L, Zhu G, Li P, Lu X, Wei W (2021) U-net based analysis of MRI for Alzheimer’s disease diagnosis. Neural Comput Appl 33(20):13587–13599
Gulli A, Kapoor A, Pal S (2019) Deep learning with TensorFlow 2 and Keras: regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API. Packt Publishing Ltd
Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean JB, Zheng X (2016) {TensorFlow}: a system for {Large-Scale} machine learning. In: 12th USENIX symposium on operating systems design and implementation, vol 16. OSDI, pp 265–283
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Singh, N., Patteshwari, D., Soni, N., Kapoor, A. (2023). Detection of Alzheimer Disease Using MRI Images and Deep Networks—A Review. In: Mishra, A., Gupta, D., Chetty, G. (eds) Advances in IoT and Security with Computational Intelligence. ICAISA 2023. Lecture Notes in Networks and Systems, vol 755. Springer, Singapore. https://doi.org/10.1007/978-981-99-5085-0_15
Download citation
DOI: https://doi.org/10.1007/978-981-99-5085-0_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-5084-3
Online ISBN: 978-981-99-5085-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)