No abstract

Skin cancer is a major public health problem, with millions newly diagnosed cases each year. Melanoma is the deadliest form of skin cancer, responsible for the most over 6500 deaths each year in the US, and the rates have been rising rapidly over years. Because of this, a lot of research is being done in automated image-based systems for skin lesion classification. In our paper we propose an automated melanoma and seborrheic keratosis recognition system, which is based on pre-trained deep network combined with structural features. We compare using different pre-trained deep networks, analyze the impact of using patient data in our approach, and evaluate our system performance with different datasets. Our results shown us that patient data has impact on characteristic curve metric value with around 2-6% and different algorithm in final classification layer has impact with around 1-4%.