Analysis of Skin Cancer Detection using Machine Learning Algorithms

Thesis Abstract

Abstract
Skin cancer is a prevalent and potentially life-threatening disease that affects millions of individuals worldwide. Early detection and accurate diagnosis are crucial for successful treatment outcomes. With the advancements in technology, machine learning algorithms have shown promise in improving the accuracy and efficiency of skin cancer detection. This thesis presents a comprehensive analysis of skin cancer detection using machine learning algorithms. The study begins with an introduction that outlines the significance of the research, followed by a background of the study that provides a context for understanding skin cancer detection and the role of machine learning algorithms in this domain. The problem statement highlights the challenges faced in current skin cancer detection methods and motivates the need for a more accurate and efficient approach. The objectives of the study are to develop and evaluate machine learning models for skin cancer detection, assess their performance against traditional methods, and identify key factors influencing their effectiveness. The limitations of the study are discussed, along with the scope of the research, which focuses on the application of machine learning algorithms to melanoma and non-melanoma skin cancer detection. A detailed literature review is conducted in Chapter Two, covering ten key areas related to skin cancer detection, machine learning algorithms, feature extraction techniques, and performance evaluation metrics. This review provides a comprehensive overview of existing research and sets the foundation for the methodology employed in this study. Chapter Three presents the research methodology, including data collection, preprocessing, feature selection, model development, and performance evaluation. The chapter also discusses the experimental setup, dataset used, evaluation metrics, and validation techniques employed to assess the performance of the machine learning models. In Chapter Four, the findings of the study are discussed in detail, including the performance comparison of different machine learning algorithms, the impact of feature selection on model accuracy, and the key factors influencing the detection of melanoma and non-melanoma skin cancers. The results are analyzed and interpreted to draw meaningful conclusions. Finally, Chapter Five concludes the thesis by summarizing the key findings, discussing the implications of the research, and suggesting future directions for improving skin cancer detection using machine learning algorithms. The study contributes to the growing body of literature on skin cancer detection and provides valuable insights for researchers, clinicians, and policymakers working in the field of dermatology and machine learning.