  In this paper, we provide a guideline for using the Neural Network
Dependability Kit (NNDK) during the development process of NN models, and show
how the algorithm is applied in two image classification use cases. The case
studies demonstrate the usage of the dependability kit to obtain insights about
the NN model and how they informed the development process of the neural
network model. After interpreting neural networks via the different metrics
available in the NNDK, the developers were able to increase the NNs' accuracy,
trust the developed networks, and make them more robust. In addition, we
obtained a novel application-oriented technique to provide supporting evidence
for an NN's classification result to the user. In the medical image
classification use case, it was used to retrieve case images from the training
dataset that were similar to the current patient's image and could therefore
act as a support for the NN model's decision and aid doctors in interpreting
the results.
