Summary
Melanoma skin cancer is the most aggressive type of skin cancer. It is most commonly caused by excessive exposure to Ultraviolet radiation which triggers uncontrollable proliferation of melanocytes. Early detection makes melanoma relatively easily curable. Diagnosis is usually done using traditional methods such as dermoscopy which consists of a manual examination performed by the physician. However, these methods are not always well founded because they depend heavily on the physician’s experience. Hence, there is a great need for a new automated approach in order to make diagnosis more reliable. In this paper, we present a twophase technique to classify images of lesions into benign or malignant. The first phase consists of an image processing-based method that extracts the Asymmetry, Border Irregularity, Color Variation and Diameter of a given mole. The second phase classifies lesions using a Genetic Algorithm. Our technique shows a significant improvement over other well-known algorithms and proves to be more stable on both training and testing data.
Keywords
Genetic algorithms - melanoma - disease prediction - machine learning - classification - cancer - image processing
