Abstract
Nowadays, the occurrence of skin cancer cases has grown worldwide due to the extended exposure to the harmful radiation from the Sun. Most common approach to detect the malignancy of skin moles is by visual inspection performed by an expert dermatologist, using a set of specific clinical rules. Computer-aided diagnosis, based on skin mole imaging, is another concurrent method which has experienced major advancements due to improvement of imaging sensors and processing power. However, these schemes use hand-crafted features which are difficult to tune and perform poorly on new cases due to lack of generalization power. In this study we present a method that use a pretrained deep neural network (DNN) to automatically extract a set of representative features that can be later used to diagnose a sample of skin lesion for malignancy. The experimental tests carried out on a clinical dataset show that the classification performance using DNN-based features performs better than the state-of-the-art techniques.