  Melanoma is not the most common form of skin cancer, but it is the most
deadly. Currently, the disease is diagnosed by expert dermatologists, which is
costly and requires timely access to medical treatment. Recent advances in deep
learning have the potential to improve diagnostic performance, expedite urgent
referrals and reduce burden on clinicians. Through smart phones, the technology
could reach people who would not normally have access to such healthcare
services, e.g. in remote parts of the world, due to financial constraints or in
2020, COVID-19 cancellations. To this end, we have investigated various
transfer learning approaches by leveraging model parameters pre-trained on
ImageNet with finetuning on melanoma detection. We compare EfficientNet,
MnasNet, MobileNet, DenseNet, SqueezeNet, ShuffleNet, GoogleNet, ResNet,
ResNeXt, VGG and a simple CNN with and without transfer learning. We find the
mobile network, EfficientNet (with transfer learning) achieves the best mean
performance with an area under the receiver operating characteristic curve
(AUROC) of 0.931$\pm$0.005 and an area under the precision recall curve (AUPRC)
of 0.840$\pm$0.010. This is significantly better than general practitioners
(0.83$\pm$0.03 AUROC) and dermatologists (0.91$\pm$0.02 AUROC).
