  Deep learning has shown outstanding performance in identifying intricate
structures in complex high-dimensional data, especially in the domain of
computer vision. The application of deep learning to early detection and
automated classification of Alzheimer's disease (AD) has recently gained
considerable attention, as rapid progress in neuroimaging techniques has
generated large-scale multimodal neuroimaging data. A systematic review of
publications using deep learning approaches and neuroimaging data for
diagnostic classification of AD was performed. A PubMed and Google Scholar
search was used to identify deep learning papers on AD published between
January 2013 and July 2018. These papers were reviewed, evaluated, and
classified by algorithm and neuroimaging type, and the findings were
summarized. Of 16 studies meeting full inclusion criteria, 4 used a combination
of deep learning and traditional machine learning approaches, and 12 used only
deep learning approaches. The combination of traditional machine learning for
classification and stacked auto-encoder (SAE) for feature selection produced
accuracies of up to 98.8% for AD classification and 83.7% for prediction of
conversion from mild cognitive impairment (MCI), a prodromal stage of AD, to
AD. Deep learning approaches, such as convolutional neural network (CNN) or
recurrent neural network (RNN), that use neuroimaging data without
preprocessing for feature selection have yielded accuracies of up to 96.0% for
AD classification and 84.2% for MCI conversion prediction. The best
classification performance was obtained when multimodal neuroimaging and fluid
biomarkers were combined. AD research that uses deep learning is still
evolving, improving performance by incorporating additional hybrid data types,
increasing transparency with explainable approaches that add knowledge of
specific disease-related features and mechanisms.
