Skin Cancer Prediction By Ml

Authors: Rohini A Zambare, N.S.Kulkarni

Abstract: Skin cancer is one of the most commonly diagnosed cancers worldwide, and early detection significantly improves treatment outcomes. Machine learning (ML) has emerged as a powerful tool in medical diagnostics, offering accurate, efficient, and scalable solutions for skin cancer prediction. This paper presents a comprehensive approach to classifying skin lesions using ML models such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forests. Using datasets like HAM10000 and ISIC, we analyze performance metrics including accuracy, precision, recall, and F1-score. The experimental results show that CNN-based models outperform traditional ML algorithms in detecting melanoma and other skin cancers. This study demonstrates the potential of AI-assisted dermatological diagnosis, thereby contributing to improved clinical workflows and patient outcomes.