A decision support system for the diagnosis of melanoma: A comparative approach

Melanoma is the most deathful of all skin cancers and the number of cases grows every year. The extirpation in early phases implies a high degree of survival so it is fundamental to diagnose it as soon as possible. In this paper we present a clinical decision support system for melanoma diagnosis using as input an image set of the skin lesion to be diagnosed. The system analyses the image sequence to extract the affected area, determinates the characteristics which indicate the degree of damage and, according to them, it makes a decision. Several methods of classification are proposed: a multilayered perceptron, a Bayesian classifier and the algorithm of the K nearest neighbours. These methods work independently and also in combination making a collaborative decision support system. The classification rates obtained are around 87%.

► Early diagnosis of melanoma is basic to increase the survival rate for this type of cancer.
► Decision support systems can aid in melanoma diagnosis using images.
► We use a set of features based on the ABCD rule.
► We make a comparison between different classifiers including a collaborative method.
► Best results are from collaborative method reaching a 87.76% of classification rate.