Probing Skin Cancer Awareness: Insights in to Classification Approaches

Skin cancer remains a significant global health concern, with its prevalence exceeding the combined cases of colon, lung, and breast cancer in the United States alone. Early detection is paramount, particularly for melanoma, where a prompt diagnosis can significantly improve patient outcomes. This paper delves into the application of machine learning (ML) and deep learning (DL) methodologies for enhanced skin cancer detection. We systematically compare various classification techniques and analyze their performance. Our primary objective is to critically evaluate existing practices and identify avenues for improvement. This exploration aims to pave the way for the development of next-generation models that can streamline the diagnostic process and ultimately reduce the time between skin cancer detection and treatment initiation.