An Automated and Interpretable Computer-Aided Approach for Skin Cancer Diagnosis Using Genetic Programming

Malignant melanoma is a very deadly form of skin cancer and early diagnosis can significantly reduce the mortality rate. Many computer-aided diagnosis (CAD) systems have been developed as second opinion diagnostic aids to assist dermatologists in diagnosing malignant melanoma. However, traditional CAD systems often require domain knowledge for feature extraction, while neural network-based CAD systems require specialized knowledge for designing network structures and often have poor interpretability. In this paper, we propose a new skin cancer CAD system based on genetic programming (GP) to automatically learn effective features for classification with strong interpretability. The approach can automatically evolve variable-length models to extract informative features for describing skin cancer images based on a relatively simple program structure, a new function set, and a terminal set. In addition, compared with other GP methods, this approach employs a newly proposed duplicate subtree removing mechanism, which can effectively prevent the duplication of features, thereby simplifying the model and enhancing its interpretability. The proposed approach has been examined on five real-world skin cancer classification tasks. The results suggest that the proposed approach achieves better performance than GP-based, neural network-based feature learning comparison methods and traditional comparison methods in most cases. Further analysis shows that the proposed approach has employed a smaller tree structure and can automatically evolve/learn models with potentially high interpretability.