This survey provides exploration of machine learning (ML) and deep learning (DL) techniques applied to skin cancer classification, highlighting the potential for revolutionizing diagnostic accuracy and efficiency. The paper categorizes different types of dermatological images and analyses public datasets, like HAM10000, which play a crucial role in the training of robust classification models. It explores state-of-the-art approaches, including convolutional neural networks, which automatically learn complex patterns and features from dermoscopic images, outperforming the traditional machine learning algorithms. The key challenges addressed include data imbalance, the scarcity of annotated datasets, computational complexity, and the need for domain adaptation to improve model generalization. The survey also emphasizes the importance of interpretability and trust in AI systems for clinical adoption, pointing out how explainable models can improve confidence among healthcare professionals. Issues related to model robustness and scalability, especially in diverse and resource-constrained clinical environments, are well discussed. At the end, a concluding discussion summarizes current trends, in terms of proposing future directions to fill the gap from light weight multimodal, easily and seamlessly integrated to workflows into the real world and ultimately filling the gap by realizing innovative research into reality- the efficient, accessible and reliable system for skin cancer diagnostic which ensures better patient care outcome.

Deep Learning Skin Cancer Classification Data Quality Computational Complexity Model Interpretability Diagnostic Tool