Multi-tree Genetic Programming with A New Fitness Function for Melanoma Detection

The occurrence of malignant melanoma had enormously increased since past decades. For accurate detection and classification, not only discriminative features are required but a properly designed model to combine these features effectively is also needed. In this study, the multi-tree representation of genetic programming (GP) has been utilised to effectively combine different types of features and evolve a classification model for the task of melanoma detection. Local binary patterns have been used to extract pixel-level informative features. For incorporating the properties of ABCD (asymmetrical property, border shape, color variation and geometrical characteristics) rule of dermoscopy, various features have been used to include local and global information of the skin lesions. To meet the requirements of the proposed multi-tree GP representation, genetic operators such as crossover and mutation are designed accordingly. Moreover, a new weighted fitness function is designed to evolve better GP individuals having multiple trees influencing each other’s performance during the evolution, in order to get overall performance gains. The performance of the new method is checked on two benchmark skin image datasets, and compared with six widely used classification algorithms and the single tree GP method. The experimental results have shown that the proposed method has significantly outperformed all these classification methods.