Skin Melanoma Cancer Detection and Classification using Machine Learning

Abstract

Skin cancer is a common and potentially life-threatening disease that affects millions of people worldwide. Early detection and accurate classification of skin lesions are critical for effective therapy and improved patient outcomes. In recent years, advances in machine learning and computer vision techniques have shown promising results in automating skin cancer detection and classification. The goal of this project is to develop an automated system for skin cancer detection and classification using machine learning algorithms. The proposed system uses a dataset of dermatoscopic images collected from different sources covering different types of skin lesions, including malignant melanoma, basal cell carcinoma, and squamous cell carcinoma. The project includes several main stages. First, preprocessing techniques including noise reduction, normalization, and feature extraction are used to improve image quality. Next, a comprehensive set of features such as color, texture, and portrait features are extracted from the preprocessed images. These features are input to various machine learning models, including Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs) and Random Forests.