Comparative Analysis of Machine Learning Classifiers for Melanoma Skin Cancer Detection

Skin cancer is the most widespread and serious form of cancer in individuals. One kind of skin cancer that is severe is melanoma. If identified in its early stages, it is easily preventable. The biopsy procedure is the recommended way to diagnose and detect melanoma. This technique can be a highly time-consuming and unbearable procedure. This work provides a computer-aided detection approach for melanoma early detection. For the purpose to categorize melanoma skin cancer, we have compared various machine learning classifiers in the present study. Support vector machines (SVM), K-nearest Neighbors (KNN), Random Forest, and Naïve Bayes have all been employed for it. We used melanoma images to compare the results we obtained from these four classifiers.