Abstract

Skin cancer is one of the most common forms of cancer, with early detection being crucial for successful treatment and improved patient outcomes. Traditional diagnostic methods, which heavily rely on the expertise of dermatologists, often face challenges such as variability in interpretation and the potential for missed early-stage malignancies. This paper presents an innovative approach to skin cancer detection using advanced machine learning techniques, aiming to enhance diagnostic accuracy and support clinical decision-making. The primary objective of this research is to develop a robust machine learning-based system capable of accurately classifying dermoscopic images of skin lesions into malignant and benign categories. Leveraging a comprehensive dataset of dermoscopic images, various preprocessing techniques were employed to enhance image quality and ensure the reliability of the training data. Multiple machine learning models, including convolutional neural networks (CNNs), were implemented and evaluated to determine the most effective approach for skin cancer detection. The results of this study demonstrate that the proposed machine learning models achieve high accuracy in classifying skin lesions, with performance metrics surpassing those of traditional diagnostic methods. The comparative analysis of different models highlights the strengths and limitations of each approach, providing valuable insights for future research and development in this field.