Melanoma Classification Through Deep Ensemble Learning and Explainable AI

Recent advancements in artificial intelligence have significantly enhanced the early detection of melanoma, a notably aggressive form of skin cancer. Deep learning systems have demonstrated high accuracy in identifying melanoma lesions, which is critical for timely and effective treatment. Despite these promising results, the challenge of explainability in AI models remains a significant concern within the research community. Efforts are ongoing to develop explainable AI techniques that can provide clearer insights into the decision-making processes of these deep learning systems. This focus on transparency aims to maximize the benefits of AI technologies while addressing trust and reliability issues. As melanoma detection accuracy improves, balancing performance with interpretability continues to be a key area of development. These advancements collectively represent a meaningful step forward in leveraging AI for medical diagnostics.