  Skin cancer is the most common cancer worldwide, with melanoma being the
deadliest form. Dermoscopy is a skin imaging modality that has shown an
improvement in the diagnosis of skin cancer compared to visual examination
without support. We evaluate the current state of the art in the classification
of dermoscopic images based on the ISIC-2019 Challenge for the classification
of skin lesions and current literature. Various deep neural network
architectures pre-trained on the ImageNet data set are adapted to a combined
training data set comprised of publicly available dermoscopic and clinical
images of skin lesions using transfer learning and model fine-tuning. The
performance and applicability of these models for the detection of eight
classes of skin lesions are examined. Real-time data augmentation, which uses
random rotation, translation, shear, and zoom within specified bounds is used
to increase the number of available training samples. Model predictions are
multiplied by inverse class frequencies and normalized to better approximate
actual probability distributions. Overall prediction accuracy is further
increased by using the arithmetic mean of the predictions of several
independently trained models. The best single model has been published as a web
service.
