Genetic Programming for Malignancy Diagnosis From Breast Cancer Histopathological Images: A Feature Learning Approach

Identifying breast cancer using histopathological images is crucial for early detection and treatment of breast cancer. Histopathological images suffer from a high inter-class and intra-class variability, making breast cancer identification a challenging task. Integration of well-developed feature descriptors into learning algorithms can enhance the automatic extraction of high-level features from these images. With its flexible representation and global search abilities, Genetic Programming (GP) is a good learning algorithm to potentially accomplish this goal. This paper proposes a new GP-based feature learning method for automatically selecting and combining various image descriptors to detect breast cancer from histopathological images, which is an emerging topic in computational intelligence. In the new approach, various global features can be learned for the task of breast cancer image classification and it is capable of automatically evolving solutions. A significant improvement in the classification performance comes from the new approach compared to the existing methods on real-world histopathological image dataset. Taking a closer look at the evolved solutions helps identify the most effective feature descriptors for breast cancer image classification. Unlike the black-box models evolved in existing methods, the proposed method evolves models/solutions that can assist dermatologists in making diagnoses by identifying breast cancer characteristics captured by the feature descriptors that are automatically selected during the evolutionary process.