The DOI matches its Crossref record; 293 of the 304 references it cites were found in a registry or an index.
Deep learning for Alzheimer's disease diagnosis: A survey
Abstract
Alzheimer's Disease (AD) is an irreversible neurodegenerative disease that results in a progressive decline in cognitive abilities. Since AD starts several years before the onset of the symptoms, its early detection is challenging due to subtle changes in biomarkers mainly detectable in different neuroimaging modalities. Developing computer-aided diagnostic models based on deep learning can provide excellent opportunities for the analysis of different neuroimage modalities along with other non-image biomarkers. In this survey, we perform a comparative analysis of about 100 published papers since 2019 that employ basic deep architectures such as CNN, RNN, and generative models for AD diagnosis. Moreover, about 60 papers that have applied a trending topic or architecture for AD are investigated. Explainable models, normalizing flows, graph-based deep architectures, self-supervised learning, and attention mechanisms are considered. The main challenges in this body of literature have been categorized and explained from data-related, methodology-related, and clinical adoption aspects. We conclude our paper by addressing some future perspectives and providing recommendations to conduct further studies for AD diagnosis.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Deep learning models have demonstrated strong performance in AD classification and risk prediction and show growing potential for inferring cellular characteristics from imaging phenotypes.”
From Diffusion MRI reveals amyloid plaque–associated cellular signatures through spatial transcriptomic integration (2026) · cites this paper as [35]
Citations
Cites
2 retracted
Diagnosis of Alzheimer Disease Using 2D MRI Slices by Convolutional Neural Network
Al-Khuzaie, F. E. K., Bayat, O., & Duru, A. D. (2021) · Applied Bionics and Biomechanics · doi:10.1155/2021/6690539 · Cited by 89
RetractedWang, S., Zhou, Q., Yang, M., & Zhang, Y. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.687456 · Cited by 86
RetractedHochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930
Generative adversarial networks
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020) · Communications of the ACM · doi:10.1145/3422622 · Cited by 18,384
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332
A survey on Image Data Augmentation for Deep Learning
Shorten, C., & Khoshgoftaar, T. M. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0197-0 · Cited by 13,203
Conditional Generative Adversarial Nets
Mirza, M., & Osindero, S. (2014) · arXiv · doi:10.48550/arxiv.1411.1784 · Cited by 8,863
Research works printed but not linked: 19
- Benyoussef, 2020, 1, 12
- Mehmood, 2020, A deep Siamese convolution neural network for multi-class classification of Alzheimer disease, Brain Sci, 10, 84, 10.3390/brainsci10020084 · DOI · Paper page
- Swain, 2020, Automatic recognition of the early stage of Alzheimer's disease based on discrete wavelet transform and reduced deep convolutional neural, Network, 531
- Haque, 2021, Research scholar, a deep learning model in the detection of Alzheimer disease, Tech Rep, 10
- Basu, 2019, 1, 205
- A. Shrikumar, P. Greenside, A. Shcherbina, A. Kundaje, Not Just a Black Box: Learning Important Features Through Propagating Activation Differences. http://arxiv.org/abs/1605.01713. · arXiv
- N. Wang, M. Chen, K. P. Subbalakshmi, Explainable cnn-attention networks (c-attention network) for automated detection of alzheimer’s disease, arXiv preprint arXiv:2006.14135. · arXiv
- T. F. A. van der Ouderaa, D. E. Worrall, Reversible GANs for Memory-efficient Image-to-Image Translation, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. URL http://arxiv.org/abs/1902.02729. · arXiv
- S. Shurrab, R. Duwairi, Self-supervised learning methods and applications in medical imaging analysis: A survey. URL http://arxiv.org/abs/2109.08685. · arXiv
- D. Bahdanau, K. Cho, Y. Bengio, Neural Machine Translation by Jointly Learning to Align and Translate. http://arxiv.org/abs/1409.0473. · arXiv
- H.-C. Shin, A. Ihsani, S. Mandava, S. T. Sreenivas, C. Forster, J. Cha, A. D. N. Initiative, GANBERT: Generative Adversarial Networks with Bidirectional Encoder Representations from Transformers for MRI to PET synthesis. http://arxiv.org/abs/2008.04393. · arXiv
- E. Jun, S. Jeong, D.-W. Heo, H.-I. Suk, Medical Transformer: Universal Brain Encoder for 3D MRI Analysis. http://arxiv.org/abs/2104.13633. · arXiv
- A. Ashraf S. Khan N. Bhagwat M. Chakravarty B. Taati , Learning to unlearn: Building immunity to dataset bias in medical imaging studies, arXiv preprint arXiv:1812.01716. · arXiv
- Z. Zhu, J. Novikova, F. Rudzicz, Deconfounding age effects with fair representation learning when assessing dementia, arXiv preprint arXiv:1807.07217. · arXiv
- Wen, 2020, Convolutional neural networks for classification of Alzheimer's disease: overview and reproducible evaluation, Med Image Anal, 63, 10.1016/j.media.2020.101694 · DOI · Paper page
- T. Bai, J. Luo, J. Zhao, B. Wen, Q. Wang, Recent Advances in Adversarial Training for Adversarial Robustness. URL http://arxiv.org/abs/2102.01356. · arXiv
- S. Chen, K. Ma, Y. Zheng, Med3D: Transfer Learning for 3D Medical Image Analysis. URL http://arxiv.org/abs/1904.00625. · arXiv
- Li, 2021, Image-driven classification of functioning and nonfunctioning pituitary adenoma by deep convolutional neural networks, computational and structural, Biotechnol J, 19, 3077
- Yang, 2018, Vol. 2, 174
Cited by
Sorted by citations
Deep learning: systematic review, models, challenges, and research directions
Khoei, T. T., Slimane, H. O., & Kaabouch, N. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08957-4 · Cited by 501
Classification of Alzheimer’s disease using MRI data based on Deep Learning Techniques
Sorour, S. E., El-Mageed, A. A. A., Albarrak, K. M., Alnaim, A. K., Wafa, A. A., & El-Shafeiy, E. (2024) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2024.101940 · Cited by 157
Alzheimer’s Disease Detection Using Deep Learning on Neuroimaging: A Systematic Review
Alsubaie, M. G., Luo, S., & Shaukat, K. (2024) · Machine Learning and Knowledge Extraction · doi:10.3390/make6010024 · Cited by 156
Elazab, A., Wang, C., Abdel-Aziz, M. H., Zhang, J., Gu, J. J., Górriz, J. M., Zhang, Y., & Chang, C. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124780 · Cited by 112
Microglia and Astrocytes in Alzheimer’s Disease: Significance and Summary of Recent Advances
Deng, Q., Wu, C., Parker, E., Liu, T. C.-Y., Duan, R., & Yang, L. (2023) · Aging and disease · doi:10.14336/ad.2023.0907 · Cited by 102
Metadata
- Authors
- Khojaste-Sarakhsi, M., Haghighi, S. S., Ghomi, S. M. T. F., & Marchiori, E.
- Affiliations
- Radboud University Nijmegen (NL)
- Amirkabir University of Technology (IR)
- Venue
- Artificial Intelligence in Medicine
- Publisher
- Elsevier BV
- Published
- 2022-08
- Volume
- 130
- Pages
- 102332
- ISSN
- 0933-3657 (ISSN-L)
- OpenAlex
- W4281730963
- Cited by
- 194
- References
- 304
- Citations by year
- 2026: 26 · 2025: 75 · 2024: 51 · 2023: 39 · 2022: 3
- FWCI
- 7.23
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Artificial Intelligence in Medicine (publishedVersion)
- PubMed (publishedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- Alzheimer's disease, computer-aided diagnosis, deep learning, neuroimaging, convolutional neural networks, recurrent neural networks, generative models, normalizing flows, graph neural networks, self-supervised learning, attention mechanism, explainable models, biomarkers, early detection, clinical adoption
- MeSH terms
- Deep Learning, Alzheimer Disease, Humans, Magnetic Resonance Imaging, Neural Networks, Computer, Neurodegenerative Diseases
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
