Using Machine Learning and Deep Learning Techniques for Predicting Melanoma Risks

Abstract

This study investigates the use of machine learning and deep learning techniques to predict the progression of melanoma, a highly aggressive form of skin cancer known for its rapid spread to vital organs. The research reviews various studies that have employed diverse datasets, including clinical and serological data, whole slide images, and lymphocytes, to enhance the accuracy of predictive models. The models utilized in these studies show promising potential for improving early diagnosis and aiding treatment decision-making, ultimately leading to better survival rates and reduced melanoma-related mortality. The study emphasizes the need for further research to validate these findings across larger datasets and diverse clinical settings.