Melanoma Detection in Dermoscopic Images: a Bag of Visual Words Approach

Abstract

Melanoma is a skin cancer caused by the ultraviolet radiation from the sun. If it is not detected at early stages, melanoma will become severe and more importantly it may spread to the other body organs, most commonly to the lungs, brain, liver and bones. Dermatologists look for the tell tales on the pigmented lesions (moles) on the skin to detect melanoma, or for some cases discriminate it from other skin diseases. Unfortunately, imprecise subjective analysis may result in the form of a series of biopsies which maybe not needed. Furthermore, this type of imprecision may allow a melanoma case to grow without a notice. To overcome this challenge, an automatic melanoma detection system is proposed in this study. The developed approach is based on Bag of Visual Words (BoVW) which includes both traditional and new age methods. Experimental comparisons between this novel approach and well-known convolutional neural network models show the effectiveness of the proposed model.