An Introduction to Deep Learning Research for Alzheimer’s Disease
Hoang Mark Nguyen, Narisa Nan Chu · IEEE Consumer Electronics Magazine · 2020
This tutorial explains the evolving approaches on deep learning (DL) modeling and their dependence on statistically comprehensive datasets as input in various brain scan neuroimages. Powerful visual modalities, e.g., magnetic resonance images and positron emission tomography, can show neural changes during Alzheimer's disease (AD) development. Computer vision's recent success has lent impetus to numerous DL modeling publications reporting accuracy above 90%, using AD NeuroImage (ADNI) datasets. However, several limitations exist when using DL for AD image interpretation. Due to the lack of a comprehensive dataset and medical images' complexity, there is little to no clinical value in such DL approaches. Furthermore, many of the published research results in the field are not comparable in experimenting with the ADNI datasets without well-accepted evaluation criteria. This tutorial describes the fundamentals and gaps in applying DL methodology over ADNI datasets.