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.

Thesis Overview

The project titled "Analysis of Skin Cancer Detection using Machine Learning Algorithms" aims to investigate the effectiveness of machine learning algorithms in the early detection and classification of skin cancer. Skin cancer is a significant public health concern globally, with early detection playing a crucial role in improving patient outcomes. Machine learning, a subset of artificial intelligence, has shown promise in various medical applications, including diagnostics. The research will focus on exploring different machine learning algorithms, such as support vector machines, random forests, and deep learning models, to analyze and classify skin lesions accurately. By utilizing a dataset of skin images with corresponding labels, the study aims to train and evaluate these algorithms to distinguish between benign and malignant skin lesions. The project will begin with a comprehensive review of existing literature on skin cancer detection, machine learning techniques, and related studies. This literature review will provide a theoretical foundation for understanding the current state of research in this field and identify gaps that the present study aims to address. Subsequently, the research methodology will detail the data collection process, preprocessing steps, feature extraction techniques, model training, and evaluation metrics. The methodology will outline the experimental setup, including the selection of algorithms, hyperparameter tuning, and cross-validation strategies to ensure robust model performance. The findings from the study will be presented in detail in the discussion chapter, highlighting the performance of different machine learning algorithms in skin cancer detection. The results will be analyzed, and insights into the strengths and limitations of each algorithm will be discussed. Additionally, the discussion will explore factors influencing the accuracy and reliability of the classification models. In conclusion, the project will summarize the key findings, implications for clinical practice, and recommendations for future research. The study aims to contribute to the growing body of knowledge on leveraging machine learning algorithms for skin cancer detection, ultimately improving diagnostic accuracy and patient outcomes in dermatology.