Skin Cancer Detection Based on Machine Learning

Abstract

Skin cancer, particularly melanoma, poses a significant health risk, accounting for the majority of skin cancer-related fatalities in the United States. Despite representing a small fraction of skin cancer cases, melanoma has one of the highest death rates, stressing the critical need for early detection. The American Cancer Society projects approximately 100,640 new melanoma cases and approximately 8,290 related deaths for 2024, yet also notes high survival rates when caught early. In this context, our study employs machine learning to enhance early detection, analyzing a large dataset from The International Skin Imaging Collaboration (ISIC) featuring over 53,000 images across various skin conditions. We assess Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), and Decision Tree classifiers through both binary and multiclass frameworks, with Principal Component Analysis (PCA) aiding in data dimensionality reduction and visualization. Our findings reveal SVM and K-NN as effective for binary classification, with K-NN excelling in multiclass scenarios. These results underscore the promise of machine learning in clinical settings, offering a path toward improved skin cancer diagnostic tools and underlining the importance of algorithm refinement and sophisticated data analysis techniques.