  Skin cancer affects a large population every year -- automated skin cancer
detection algorithms can thus greatly help clinicians. Prior efforts involving
deep learning models have high detection accuracy. However, most of the models
have a large number of parameters, with some works even using an ensemble of
models to achieve good accuracy. In this paper, we investigate a recently
proposed pruning technique called Lottery Ticket Hypothesis. We find that
iterative pruning of the network resulted in improved accuracy, compared to
that of the unpruned network, implying that -- the lottery ticket hypothesis
can be applied to the problem of skin cancer detection and this hypothesis can
result in a smaller network for inference. We also examine the accuracy across
sub-groups -- created by gender and age -- and it was found that some
sub-groups show a larger increase in accuracy than others.
