Skin cancer is one of the most prevalent malignant tumours worldwide, and its incidence has continued to climb in recent years. Traditional feature extraction methods often struggle with the high variability and complex patterns in skin cancer images, necessitating more adaptive and automated approaches. This study proposes a genetic programming (GP)-based method with flexible region detection operators (GPFRD) for automatically and flexibly learning discriminative features for various classification tasks of skin cancer images. The proposed GPFRD method integrates preprocessing, region detection, feature extraction, and feature concatenation into a cohesive framework, significantly enhancing flexibility. The newly designed operators precisely localize diagnostically critical regions based on lesion masks while suppressing irrelevant background interference. These operators enable the proposed method to evolve effective feature extraction solutions based on the characteristics of different image datasets. Experimental results on five datasets of varying difficulties demonstrate that the proposed method outperforms the benchmark GP-based method and four traditional feature extraction methods in the majority of cases.