Deep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review. (April 2020)

• Record Type: Journal Article
• Authors: Ebrahimighahnavieh, Mr Amir; Luo, Suhuai; Chiong, Raymond
• Is Part Of: Computer methods and programs in biomedicine. Volume 187(2020)
• Publication Date: 2020-04
• Subjects: Deep learning -- Alzheimer's disease -- Convolutional neural networks -- Recurrent neural networks -- Auto-encoders -- Transfer learning; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.105242
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A review of Alzheimer's disease (AD) detection using deep learning is carried out. Intensity normalization & registration are key preprocessing methods in AD detection. Patch-based methods on disease-related regions are more useful for feature extraction. Convolutional neural networks are increasingly utilized with impressive results. Transfer learning and data augmentation are useful with a limited number of patients. 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.