Deep Convolutional Neural Networks With Ensemble Learning and Generative Adversarial Networks for Alzheimer's Disease Image Data Classification

Robert Logan, Brian G. Williams, Maria Ferreira da Silva, Akash Indani, Nicolas Schcolnicov, Anjali Ganguly, Sean J. Miller
2021 Frontiers in Aging Neuroscience  
Recent advancements in deep learning (DL) have made possible new methodologies for analyzing massive datasets with intriguing implications in healthcare. Convolutional neural networks (CNN), which have proven to be successful supervised algorithms for classifying imaging data, are of particular interest in the neuroscience community for their utility in the classification of Alzheimer's disease (AD). AD is the leading cause of dementia in the aging population. There remains a critical unmet
more » ... for early detection of AD pathogenesis based on non-invasive neuroimaging techniques, such as magnetic resonance imaging (MRI) and positron emission tomography (PET). In this comprehensive review, we explore potential interdisciplinary approaches for early detection and provide insight into recent advances on AD classification using 3D CNN architectures for multi-modal PET/MRI data. We also consider the application of generative adversarial networks (GANs) to overcome pitfalls associated with limited data. Finally, we discuss increasing the robustness of CNNs by combining them with ensemble learning (EL).
doi:10.3389/fnagi.2021.720226 pmid:34483890 pmcid:PMC8416107 fatcat:3bvka3lkunesxhehbpgqctolta