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

## TL;DR

This paper introduces a novel method for Parkinson's disease diagnosis by extracting latent features from deep neural networks and combining transfer learning, clustering, and classification techniques to improve prediction accuracy.

## Contribution

It proposes a new approach that leverages latent representations from DNNs with a custom loss function for better disease prediction across different medical environments.

## Key findings

- Effective prediction of Parkinson's using MRI and DaT Scan data.
- Enhanced representation adaptation with the new loss function.
- Validated on real hospital data with promising results.

## 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, or convolutional-recurrent Neural Networks (DNNs). In particular, our approach adopts a combination of transfer learning, k-means clustering and k-Nearest Neighbour classification of deep 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.