Deep Neural Networks with Broad Views for Parkinson's Disease Screening

Abstract

Parkinson's Disease (PD) is a progressive neurodegenerative disorder, which is characterized by motor symptoms. In recent years, machine learning based approaches have been proposed to assist the diagnosis of PD. However, existing approaches mainly concerned classification task and mostly studied using single-view data. In this paper, we tackle PD screening task using multi-view data. A PD screening task aims to use the diagnostic data from brain magnetic resonance imaging (MRI) as an assistance to prevent and delay the deterioration of PD. To perform this task, we propose a novel deep learning architecture called Deep neural networks with Broad Views (DBV). The proposed model builds upon Wasserstein Generative Adversarial Networks (WGAN) and ResNeXt, which can exploit multi-view data jointly. Experimental results using multi-view brain MRI data from the Parkinson's Progression Markers Initiative (PPMI) database show that the proposed model outperforms several existing solid deep learning baselines dramatically.