Abstract

In a world witnessing an increase in skin cancer cases, early identification is pivotal for improved treatment outcomes. Skin cancer, propelled by factors like increased sun exposure and evolving lifestyles, manifests in various types, each requiring tailored treatment approaches. With different types of skin cancer being either malignant or benign, it can become crucial to identify the type to prepare a roadmap for its treatment. Our approach underscores the critical need for under- standing and addressing skin cancer, emphasizing the importance of early detection. It integrates machine learning and image analysis, enabling users to promptly identify and understand skin conditions through simple image capture. This project explores the development, implementation, and evaluation of machine learning models for skin cancer detection, emphasizing their potential to revolutionize early diagnosis.