The DOI matches its Crossref record; 203 of the 203 references it cites were found in a registry or an index.
Deep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review
Abstract
Alzheimer's Disease (AD) is one of the leading causes of death in developed countries. From a research point of view, impressive results have been reported using computer-aided algorithms, but clinically no practical diagnostic method is available. In recent years, deep models have become popular, especially in dealing with images. Since 2013, deep learning has begun to gain considerable attention in AD detection research, with the number of published papers in this area increasing drastically since 2017. Deep models have been reported to be more accurate for AD detection compared to general machine learning techniques. Nevertheless, AD detection is still challenging, and for classification, it requires a highly discriminative feature representation to separate similar brain patterns. This paper reviews the current state of AD detection using deep learning. Through a systematic literature review of over 100 articles, we set out the most recent findings and trends. Specifically, we review useful biomarkers and features (personal information, genetic data, and brain scans), the necessary pre-processing steps, and different ways of dealing with neuroimaging data originating from single-modality and multi-modality studies. Deep models and their performance are described in detail. Although deep learning has achieved notable performance in detecting AD, there are several limitations, especially regarding the availability of datasets and training procedures.
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Cites
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Cited by
Borges, A. F. S., Laurindo, F. J. B., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102225 · Cited by 760
Grueso, S., & Viejo-Sobera, R. (2021) · Alzheimer's Research & Therapy · doi:10.1186/s13195-021-00900-w · Cited by 303
Deep learning for Alzheimer's disease diagnosis: A survey
Khojaste-Sarakhsi, M., Haghighi, S. S., Ghomi, S. M. T. F., & Marchiori, E. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102332 · Cited by 194
Functional magnetic resonance imaging, deep learning, and Alzheimer's disease: A systematic review
Warren, S. L., & Moustafa, A. A. (2023) · Journal of Neuroimaging · doi:10.1111/jon.13063 · Cited by 161
Convolutional neural networks for Alzheimer’s disease detection on MRI images
Ebrahimi, A., & Luo, S. (2021) · Journal of Medical Imaging · doi:10.1117/1.jmi.8.2.024503 · Cited by 133
Self-citation
Metadata
- Authors
- Ebrahimighahnavieh, M. A., Luo, S., & Chiong, R.
- Venue
- Computer Methods and Programs in Biomedicine
- Publisher
- Elsevier BV
- Published
- 2020-04
- Volume
- 187
- Pages
- 105242
- ISSN
- 0169-2607 (ISSN-L)
- OpenAlex
- W2990581109
- Cited by
- 429
- References
- 203
- FWCI
- 7.04
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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