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- W2990581109 endingPage "105242" @default.
- W2990581109 startingPage "105242" @default.
- W2990581109 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." @default.
- W2990581109 created "2019-12-05" @default.
- W2990581109 creator A5027845071 @default.
- W2990581109 creator A5071582080 @default.
- W2990581109 creator A5078730212 @default.
- W2990581109 date "2020-04-01" @default.
- W2990581109 modified "2023-10-14" @default.
- W2990581109 title "Deep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review" @default.
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