Genetic Programming for the Automatic Construction of Features in Skin-Lesion Image Classification

This dissertation describes the design and implementation of a genetic programming system which automatically constructs feature equations for the classification of skin lesion images as a part of a real world dermatological image retrieval system. It uses generalized co-occurrence matrices (GCMs) and normal mathematical functions combined stochastically and evaluated using the feature selection techniques of fisher " s discriminant ratio, and the classification accuracy of either a bayes classifier or support vector machine. It deals with the notion of GP closure with " shell " functions and is able to arbitrarily combine information from different color channels, both unique designs compared with similar GP systems. Further, it can evolve features iteratively to complement each other. The implementation here is able to create features in small numbers which are able to classify better than most of the traditional set of Haralik features, even when the Haralik features are created with a greater number of GCM parameters. However, the system developed here does exhibit two notable problems for future work. The run-time is notably long and the amount of data collected in-house is not yet great enough to significantly measure the ability of the system to generalize. However, these problems are fixable and the work described has resulted in a system which aids classification relatively well and just as importantly, shows much potential.