Multi-view ensemble learning for dementia diagnosis from neuroimaging: An artificial neural network approach

Identifying abnormalities from neuroimaging of brain matters has been a crucial way of diagnosis of two closely associated diseases, namely Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI). Different types of neuroimaging have been developed to help such diagnosis, and significant research efforts are put into the automation and quantification of such diagnosis by computer algorithms over the past decades. In this paper we propose an ensemble learning framework to create effective models for AD/MCI related classification tasks from multiple modalities of neuroimaging and multiple baseline estimators. The framework is based on artificial neural networks and it resembles a composite model that solves the feature fusion learning problem as well as the prediction problem simultaneously, which targets at exploiting the prediction power of both fusing multiple data modalities and leveraging multiple mutually complementary classification models. We conduct extensive experiments on the well-known ADNI dataset and find that the proposed model works demonstrate advantages for both of the classification tasks studied.