  Early detection of melanoma is difficult for the human eye but a crucial step
towards reducing its death rate. Computerized detection of these melanoma and
other skin lesions is necessary. The central research question in this paper is
"How to segment skin lesion images using a neural network with low available
data?". This question is divided into three sub questions regarding best
performing network structure, training data and training method. First theory
associated with these questions is discussed. Literature states that U-net CNN
structures have excellent performances on the segmentation task, more training
data increases network performance and utilizing transfer learning enables
networks to generalize to new data better.
  To validate these findings in the literature two experiments are conducted.
The first experiment trains a network on data sets of different size. The
second experiment proposes twelve network structures and trains them on the
same data set. The experimental results support the findings in the literature.
The FCN16 and FCN32 networks perform best in the accuracy, intersection over
union and mean BF1 Score metric. Concluding from these results the skin lesion
segmentation network is a fully convolutional structure with a skip
architecture and an encoder depth of either one or two. Weights of this network
should be initialized using transfer learning from the pre trained VGG16
network. Training data should be cropped to reduce complexity and augmented
during training to reduce the likelihood of overfitting.
