Deep-Learning-Based Diagnosis and Prognosis of Alzheimer’s Disease: A Comprehensive Review

Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common cause of Dementia. Neuroimaging analyses, such as T1 weighted magnetic resonance imaging, positron emission tomography, and the deep learning (DL) approaches have attracted researchers for automated AD diagnosis in the early stages. Therefore, a review is required to understand DL algorithms to develop more efficient AD diagnosis methods. This article discusses a detailed review of automated early AD diagnosis using DL methods published from 2009 to 2022. The novelties of this article include: 1) introducing popular imaging modalities; 2) discussing early biomarkers for AD diagnosis using neuroimaging scans; 3) reviewing the popular online available data sets widely used; 4) systematically describing the various DL algorithms for accurate and early assessment of AD; 5) discussion on advantages and limitations of the DL-based model for AD diagnosis; and 6) provides an outlook toward future trends derived from our critical assessment.