Melanoma detection and classification using SVM based decision support system

Melanoma is quite a precarious form of skin cancer. The malignant skin tumors much resemble benign nevus, mole or dysplastic naevi. For dermatologists, it is a tedious task to analyze every patient sample more precisely, so it needs a decision support system to analyze the danger associated with a given sample. In this work color images of melanoma are imparted to classify them among malignant and benign classes using Support Vector Machine (SVM) optimized by Sequential Minimal Optimization(SMO). As a part of the preprocessing step, an illumination compensation based segmentation algorithm is deployed. The segmentation process is followed by the proposed iterative dilation method to remove noise from a lesion. Some prominent features calculated from the segmented image based on asymmetric lesion-behavior, border irregularity, color variations and spanned diameter. Finally, these feature vector applied as an input to SVM classifier, which is used to distinguish malignant from benign samples of skin lesions. The dataset is divided into training and testing data to account and validate the system performance.