Melanoma Skin Cancer Prediction Using Machine Learning Algorithm

Abstract

The academic paper titled "Melanoma Skin Cancer Prediction Using Machine Learning Algorithms" provides a detailed analysis of the application of machine learning techniques in the timely detection and categorization of melanoma skin cancer. The research aims to enhance the accuracy and reliability of predicting the distinction between malignant melanomas and benign lesions by utilizing a diverse set of skin lesion images. The study examines various machine learning models, including Logistic Regression, Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Decision Trees, Gaussian Naïve Bayes, and Ensemble Techniques, as well as advanced deep learning approaches like Convolutional Neural Networks (CNNs). Emphasizing the potential, the paper underscores the benefits of integrating advanced machine learning methods in clinical settings to improve the timely detection of melanoma, thus ultimately boosting patient outcomes and treatment efficacy.