Statistical Learning Approach for Robust Melanoma Screening

Abstract

According to the American Cancer Society, one person dies of melanoma every 57 minutes, although it is the most curable type of cancer if detected early. Thus, computeraided diagnosis for melanoma screening has been a topic of active research. Much of the existing art is based on the Bag-of-Visual-Words (BoVW) model, combined with color and texture descriptors. However, recent advances in the BoVW model, as well as the evaluation of the importance of the many different factors affecting the BoVW model were yet to be explored, thus motivating our work. We show that a new approach for melanoma screening, based upon the state-of-the-art BossaNova descriptors, shows very promising results for screening, reaching an AUC of up to 93.7%. An important contribution of this work is an evaluation of the factors that affect the performance of the two-layered BoVW model. Our results show that the low-level layer has a major impact on the accuracy of the model, but that the codebook size on the mid-level layer is also important. Those results may guide future works on melanoma screening.