Predicting Parkinson's Disease using Latent Information extracted from Deep Neural Networks

Predicting Parkinson's Disease using Latent Information extracted from Deep Neural Networks Abstract--This paper presents a new method for medical diagnosis of neurodegenerative diseases, such as Parkinson's, by extracting and using latent information from trained Deep convolutional, orconvolutional-recurrent Neural Networks (DNNs). In particular, our approach adopts a combination of transfer learning, k-means clustering and k-Nearest Neighbour classification ofdeep neural network learned representations to provide enriched prediction of the disease based on MRI and/or DaT Scan data. A new loss function is introduced and used in the training of the DNNs, so as to perform adaptation of the generated learned representations between data from different medical environments. Results are presented using a recently published database of Parkinson's related information, which was generated and evaluated in a hospital environment. Index Terms--latent variable information, deep convolutional and recurrent neural networks, transfer learning and domain adaptation, modified loss function, prediction, Parkinson's disease, MRI,DaT Scan data. I. INTRODUCTION Machine learning techniques have been largely used in medical signaland image analysis for prediction of neurodegenerative disorders,such as Alzheimer's and Parkinson's, which significantly affect elderly people, especially in developed countries [1], [2], [3].