Comparative Analyses of Classifiers for Diagnosis of Skin Cancer using Dermoscopic Images

In recent years one of the emerging deadliest diseases is Skin cancer. Skin cancers are of different types. But melanoma, basal cell carcinoma and squamous cell carcinoma these types are most commonly found in humans. The death rate due to skin lesions can be reduced if detected early. An efficient image analysis module has been developed with efficient algorithm to detect the skin lesions. In the analysis system classification plays an important role in identification of defect. In the proposed system different types of classifiers such as Support Vector Machine, ensemble classifier, probabilistic neural network and adaptive neuro-fuzzy inference system classifiers are used in the classification process and their performance is compared and the classifier with best performance is used in identifying the skin lesions.