Machine Learning for Skin Cancer Classification in Dermatology

Thesis Abstract

Abstract
Skin cancer is a prevalent and potentially life-threatening disease that affects millions of individuals worldwide. Early detection and accurate classification of skin lesions are crucial for successful treatment outcomes. In recent years, machine learning techniques have shown promising results in the field of dermatology, particularly in skin cancer classification. This thesis explores the application of machine learning algorithms for the automated classification of skin cancer lesions based on image analysis. The study begins with a comprehensive review of existing literature on skin cancer classification using machine learning methods. Various algorithms, such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forests, have been utilized for skin lesion classification with varying degrees of success. The literature review also discusses the importance of feature extraction and selection in improving the performance of machine learning models for skin cancer classification. The research methodology section outlines the process of data collection, preprocessing, feature extraction, model training, and evaluation. A dataset of skin lesion images will be used to train and test the machine learning models. The study will compare the performance of different algorithms in terms of accuracy, sensitivity, specificity, and other relevant metrics to identify the most effective approach for skin cancer classification. The findings of the study will be presented and discussed in detail in the results section. The performance of the machine learning models will be evaluated based on their ability to accurately classify skin lesions into benign and malignant categories. The discussion will also address the strengths and limitations of the models, as well as potential areas for future research and improvement. In conclusion, this thesis contributes to the growing body of research on machine learning applications in dermatology, specifically in the context of skin cancer classification. The study demonstrates the potential of machine learning algorithms to assist dermatologists in the early detection and accurate diagnosis of skin cancer lesions. By leveraging the power of artificial intelligence and image analysis, this research aims to improve the efficiency and accuracy of skin cancer diagnosis, ultimately leading to better patient outcomes and reduced healthcare costs.