Automatic classification of skin tumours with high resolution surface profiles

Abstract

This paper describes a new approach to automatic classification of melanocytic tumours based on features extracted from profilometric data. The clinical accuracy of dermatologists in identifying these tumours is only approximately 75%. Automatic classification is based on high resolution skin surface profiles of 4×4 mm² size with 125 sample points per mm, generated with a laser profilometer. Three categories of profile features are extracted: Textural features, Fourier features and fractal features. Feature selection is performed to determine an optimal feature subset. As a quality measure for a given feature subset, the error rate of the nearest neighbour classifier estimated with the leaving-one-out method is used. With the optimal feature subset, feed forward neural networks with error backpropagation as learning function are trained. Several neural networks with different network topologies and learning parameters were trained to compare the classification performance. A three layer network with one hidden layer consisting of 20 units has shown the best performance of all considered neural networks with a classification error rate of 13.4%. The best results using the nearest neighbour classifier achieved an error rate of 6.8%.