Evolving Feature Extraction Models for Melanoma Detection: A Co-operative Co-evolution Approach

As global mortality rates rise alongside an increasing incidence of skin cancer, it becomes increasingly clear that the pursuit of an effective strategy to combat this challenge is gaining urgency. In traditional practices, the diagnosis of skin cancer predominantly depends on manual inspection of skin lesions. Despite its prevalent use, this approach is beset with several limitations, such as subjectivity, time constraints, and the invasive nature of biopsy procedures. Addressing these obstacles, the burgeoning field of Artificial Intelligence has been instrumental in advancing Computer Automated Diagnostic Systems (CADS) for skin cancer. A critical aspect of these systems is feature extraction, a process crucial for discerning and utilising key characteristics from raw image data, thereby bolstering the efficacy of CADS. This study introduces a feature extraction model that evolves automatically, leveraging the principles of genetic programming and cooperative coevolution. This method generates a ensemble of models that collaboratively work to extract discerning features from images of skin lesions. The model’s effectiveness is evaluated using a publicly accessible dataset, whilst further analysis pertaining to interactions between the decomposition of image colour channels are explored. The findings indicate that the proposed method either matches or significantly surpasses the performance of established benchmarks and recent methodologies in this field, underscoring its potential in enhancing skin cancer diagnostic processes.