A Review on Skin Melanoma Classification using different ML and DL Models

Abstract

This review explores the classification of skin melanoma utilizing various machine learning (ML) and deep learning (DL) models, highlighting the advancements and comparative performance of these methodologies. Skin melanoma, a serious type of skin cancer, demands early and accurate diagnosis for effective treatment. The review covers a range of ML techniques such as support vector machines, decision trees, and ensemble methods, alongside \DL approaches including convolutional neural networks and recurrent neural networks. Emphasis is placed on the models' accuracy, computational efficiency, and the datasets used for training and validation. The review underscores the potential of DL models to outperform traditional ML methods due to their ability to automatically extract and learn intricate features from large datasets, thus offering promising prospects for enhanced diagnostic precision in melanoma classification.