Comparison Of Neural Networks And Statistical Methods In Melanoma Classification

: In this paper different strategies for classification are applied to automated melanoma recognition. First features are selected with a statistical approach, which are then classified by a k-nearest-neighbour classifier and a multi-layer perceptron. A third classification is done with a multi-layer perceptron on the entire feature set using a pruning strategy. The results show that the performances achieved with both the neural net (74; 5%) and the statistical approach (74; 8%) are comparable to the recognition rates of dermatologists. Furthermore it is shown that pruning the neural net or selecting a feature subset impressively reduces the complexity of the classification process and improves the generalization behaviour by removal of redundancy in the feature set. 1 Introduction Melanoma is one of the most aggressive types of cancer. Since the curability of skin cancer by surgical excision is very high in early stages the early recognition is of utmost importance. Therefore sophist..