  The extent to which the arbitrarily selected L2 regularization hyperparameter
value affects the outcome of semantic segmentation with deep learning is
demonstrated. Demonstrations rely on training U-net on small LGE-MRI datasets
using the arbitrarily selected L2 regularization values. The remaining
hyperparameters are to be manually adjusted or tuned only when 10 % of all
epochs are reached before the training validation accuracy reaches 90%.
Semantic segmentation with deep learning outcomes are objectively and
subjectively evaluated against the manual ground truth segmentation.
