Deep Neural Networks with Broad Views for Parkinsons Disease Screening Parkinsonu0027s 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 singleview data. In this paper, we tackle PD screening task using multiview 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 multiview data jointly. Experimental results using multiview brain MRI data from the Parkinsonu0027s Progression Markers Initiative (PPMI) database show that the proposed model outperforms several existing solid deep learning baselines dramatically.

Generalization Performance of Deep Learning Models in Neurodegenerative Disease Classification Over the past decade, machine learning gained considerable attention from the scientific community and has progressed rapidly as a result. Given its ability to detect subtle and complicated patterns, deep learning (DL) has been utilized widely in neuroimaging studies for medical data analysis and automated diagnostics with varying degrees of success. In this paper, we question the remarkable accuracies of the best performing models by assessing generalization performance of the stateoftheart convolutional neural network (CNN) models on the classification of two most common neurodegenerative diseases, namely Alzheimeru0027s Disease (AD) and Parkinsonu0027s Disease (PD) using MRI. We demonstrate the impact of the data division strategy on the model performances by comparing the results derived from two different split approaches. We first evaluated the performance of the CNN models by dividing the dataset at the subject level in which all of the MRI slices of a patient are put into either training or test set. We then observed that pooling together all slices prior to applying crossvalidation, as erroneously done in a number of previous studies, leads to inflated accuracies by as much as 26% for the classification of the diseases.