  Convolutional neural networks (CNN) have become a powerful tool for detecting
patterns in image data. Recent papers report promising results in the domain of
disease detection using brain MRI data. Despite the high accuracy obtained from
CNN models for MRI data so far, almost no papers provided information on the
features or image regions driving this accuracy as adequate methods were
missing or challenging to apply. Recently, the toolbox iNNvestigate has become
available, implementing various state of the art methods for deep learning
visualizations. Currently, there is a great demand for a comparison of
visualization algorithms to provide an overview of the practical usefulness and
capability of these algorithms.
  Therefore, this thesis has two goals: 1. To systematically evaluate the
influence of CNN hyper-parameters on model accuracy. 2. To compare various
visualization methods with respect to the quality (i.e. randomness/focus,
soundness).
