Evolving Ternary Patterns and Discriminative Localisation for Basal Cell Carcinoma Detection.

The rising global incidence of skin cancer, particularly Basal Cell Carcinoma (BCC), necessitates the development of automated diagnostic systems that are both accurate and interpretable for clinical adoption. While Deep Learning approaches have shown promise in medical image analysis, their “black box” nature remains a significant barrier to clinical trust and deployment. To address these challenges, this study introduces CoDED (Co-evolutionary Descriptor Engine for Detection), a genetic programming method that enhances existing automated BCC detection through two synergistic advancements. First, the method replaces traditional binary encoding with evolved Local Ternary Patterns (LTP), enabling richer textural feature extraction through adaptive dual-threshold mechanisms. Second, a novel Discriminative Localisation Mapping (DLM) technique provides pixel-level interpretability, revealing the model’s diagnostic rationale through saliency visualisations. Comprehensive experimental evaluation on clinical dermoscopy images demonstrates that CoDED achieves a balanced accuracy of 79.57