Experiments using deep learning for dermoscopy image analysis

Skin cancer is a major public health problem, as is the most common type of cancer and represents more than half of cancer diagnosed worldwide. Early detection influences the outcome of the disease and motivates the research presented in this paper. Recent results show that deep learning based approaches learn from data, and can outperform human specialists in a set of tasks when large databases are available for training. This research investigates the scenario where the amount of data available for training is small. It obtains relevant results for the ISBI 2016 melanoma classification challenge (named Skin Lesion Analysis for Melanoma Detection) facing the peculiarities of dealing with such a small and unbalanced biological database. To do this, it explores committees of Deep Convolutional Neural Networks (DCNN), the augmentation of the training data set by image processing classical transforms and by deformations guided by expert knowledge about the lesion axis, and it introduces a third class aiming to improve the classifiers' distinction of the region of interest of the lesion. The experiments show that the proposed approach improves the final classifier invariance for common melanoma variations, common skin patterns and markers, and dermatoscope capturing conditions.