Feature Extraction with Automated Scale Selection in Skin Cancer Image Classification: A Genetic Programming Approach

Early detection of cancer is vital for reducing mortality rates, but medical images come in various resolutions, often captured from diverse devices, and pose challenges due to high inter-class and intra-class variability. Integrating various feature descriptors enhances high-level feature extraction for improved classification. Having varied structure sizes of tumor characteristics in these medical images, extracting features from a single scale might not provide meaningful or discriminative features. Genetic Programming (GP) proves effective in this context due to its flexible representation and global search capabilities. Unlike existing GP methods relying on extracting features from a single scale of the input image, this paper introduces a novel GP-based feature learning approach that automatically selects scales and combines image descriptors for skin cancer detection. The method learns global features from diverse scales, leading to improved classification performance on dermoscopic and standard camera image datasets. The evolved solutions not only enhance classification but also pinpoint the most effective scales and feature descriptors for different skin cancer image datasets. The proposed method generates interpretable models, aiding medical practitioners in diagnoses by identifying cancer characteristics captured through automatically selected feature descriptors in the evolutionary process.