  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.
