Ensemble Deep Learning Models Enhance Early Diagnosis Of Alzheimer’s Disease Using Neuroimaging Data

This article discusses the use of ensemble deep learning (EDL) models in the detection of Alzheimer’s disease (AD). EDL combines the outputs of multiple machine learning models to improve generalization performance and measure uncertainty. It can overcome challenges related to data distribution and size, and can be updated easily with new information. The article categorizes and discusses different approaches to using EDL for AD detection, including slice-based and voxel-based methods, as well as single- and multi-modal methodologies.