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The incidence of malignant melanoma continues to increase worldwide. Thiscancer can strike at any age; it is one of the leading causes of loss of lifein young persons. Since this cancer is visible on the skin, it is potentiallydetectable at a very early stage when it is curable. New developments haveconverged to make fully automatic early melanoma detection a real possibility.First, the advent of dermoscopy has enabled a dramatic boost in clinicaldiagnostic ability to the point that melanoma can be detected in the clinic atthe very earliest stages. The global adoption of this technology has allowedaccumulation of large collections of dermoscopy images of melanomas and benignlesions validated by histopathology. The development of advanced technologiesin the areas of image processing and machine learning have given us the abilityto allow distinction of malignant melanoma from the many benign mimics thatrequire no biopsy. These new technologies should allow not only earlierdetection of melanoma, but also reduction of the large number of needless andcostly biopsy procedures. Although some of the new systems reported for thesetechnologies have shown promise in preliminary trials, widespreadimplementation must await further technical progress in accuracy andreproducibility. In this paper, we provide an overview of computerizeddetection of melanoma in dermoscopy images. First, we discuss the variousaspects of lesion segmentation. Then, we provide a brief overview of clinicalfeature segmentation. Finally, we discuss the classification stage wheremachine learning algorithms are applied to the attributes generated from thesegmented features to predict the existence of melanoma.

An Overview of Melanoma Detection in Dermoscopy Images Using Image Processing and Machine Learning