  Parkinson's disease (PD) is a degenerative and progressive neurological
condition. Early diagnosis can improve treatment for patients and is performed
through dopaminergic imaging techniques like the SPECT DaTscan. In this study,
we propose a machine learning model that accurately classifies any given
DaTscan as having Parkinson's disease or not, in addition to providing a
plausible reason for the prediction. This is kind of reasoning is done through
the use of visual indicators generated using Local Interpretable Model-Agnostic
Explainer (LIME) methods. DaTscans were drawn from the Parkinson's Progression
Markers Initiative database and trained on a CNN (VGG16) using transfer
learning, yielding an accuracy of 95.2%, a sensitivity of 97.5%, and a
specificity of 90.9%. Keeping model interpretability of paramount importance,
especially in the healthcare field, this study utilises LIME explanations to
distinguish PD from non-PD, using visual superpixels on the DaTscans. It could
be concluded that the proposed system, in union with its measured
interpretability and accuracy may effectively aid medical workers in the early
diagnosis of Parkinson's Disease.
