Melanoma Mirage: Unmasking Skin Cancer with Deep Learning

This article presents research on automated skin cancer detection using image processing and machine learning techniques. This paper focuses on melanoma, the deadliest form of skin cancer, and highlights the limitations of clinical observation. This study uses Convolutional Neural Networks (CNNs) and feature extraction to accurately classify skin cancers as malignant or benign. The proposed method achieves high detection accuracy by removing normal skin layers, extracting valuable information, and training/testing a classification system. Studies acknowledge the rising incidence of skin cancer, emphasizing the role of early detection. This research will contribute to efficient and accurate skin cancer detection by integrating advanced image analysis and machine learning.