Skin Cancer Classification using Image Processing and Machine Learning

One of the most rapidly spreading cancers among various other types of cancers known to humans is skin cancer. Melanoma is the worst and the most dangerous type of skin cancer that appears usually on the skin surface and then extends deeper into the layers of skin. However, if diagnosed at an early stage; the survival rate of Melanoma patients is 96% with simple and economical treatments. The conventional method of diagnosing Melanoma involves expert dermatologists, equipment, and Biopsies. To avoid the expensive diagnosis, and to assist dermatologists, the field of machine learning has proven to provide state of the art solutions for skin cancer detection at an earlier stage with high accuracy. In this paper, a method for skin lesion classification and segmentation as benign or malignant is proposed using image processing and machine learning.

Keywords: Skin lesion segmentation, contrast stretching, features extraction, features reduction, features normalization, features scaling, wrapper method, skin cancer classification, random forest classifier.