  We describe a software toolbox for the configuration of deep neural networks
in the domain of skin cancer classification. The implemented software
architecture allows developers to quickly set up new convolutional neural
network (CNN) architectures and hyper-parameter configurations. At the same
time, the user interface, manageable as a simple spreadsheet, allows
non-technical users to explore different configuration settings that need to be
explored when switching to different data sets. In future versions, meta
leaning frameworks can be added, or AutoML systems that continuously improve
over time. Preliminary results, conducted with two CNNs in the context melanoma
detection on dermoscopic images, quantify the impact of image augmentation,
image resolution, and rescaling filter on the overall detection performance and
training time.
