Published Paper Details:

Skin cancer is one of the most common types of cancer worldwide, and early detection is crucial for effective treatment. Traditional diagnostic methods rely on clinical expertise, which can be subjective and time-consuming. In recent years, machine learning (ML) techniques have gained significant attention for their potential in automating skin lesion detection and classification, improving diagnostic accuracy, and reducing human dependency. This survey provides a comprehensive review of various ML-based approaches for skin lesion analysis, including feature extraction techniques, classification algorithms, and evaluation metrics. It explores different datasets used for training ML models and highlights challenges such as data imbalance, variability in lesion appearance, and interpretability of models. Additionally, recent advancements in deep learning, particularly convolutional neural networks (CNNs), are discussed, along with their impact on improving classification performance. The survey also examines the role of ensemble learning and hybrid models in enhancing diagnostic accuracy. Finally, potential future research directions are outlined to address existing limitations and further advance ML applications in dermatology.