  Skin cancer is the most common malignancy in the world. Automated skin cancer
detection would significantly improve early detection rates and prevent deaths.
To help with this aim, a number of datasets have been released which can be
used to train Deep Learning systems - these have produced impressive results
for classification. However, this only works for the classes they are trained
on whilst they are incapable of identifying skin lesions from previously unseen
classes, making them unconducive for clinical use. We could look to massively
increase the datasets by including all possible skin lesions, though this would
always leave out some classes. Instead, we evaluate Siamese Neural Networks
(SNNs), which not only allows us to classify images of skin lesions, but also
allow us to identify those images which are different from the trained classes
- allowing us to determine that an image is not an example of our training
classes. We evaluate SNNs on both dermoscopic and clinical images of skin
lesions. We obtain top-1 classification accuracy levels of 74.33% and 85.61% on
clinical and dermoscopic datasets, respectively. Although this is slightly
lower than the state-of-the-art results, the SNN approach has the advantage
that it can detect out-of-class examples. Our results highlight the potential
of an SNN approach as well as pathways towards future clinical deployment.
