Thesis Abstract

Abstract
Skin cancer is a prevalent and potentially life-threatening disease that affects millions of people worldwide. Early detection and accurate diagnosis are crucial for successful treatment outcomes. In recent years, machine learning algorithms have shown great promise in assisting dermatologists with automated skin cancer detection. This thesis presents the development and evaluation of a novel machine learning algorithm for automated skin cancer detection. Chapter 1 provides an introduction to the research topic, background information on skin cancer, the problem statement, objectives of the study, limitations, scope, significance of the study, structure of the thesis, and definitions of key terms. Chapter 2 presents a comprehensive literature review on the current state-of-the-art in skin cancer detection using machine learning algorithms. Ten key studies are reviewed, highlighting the methodologies, algorithms, datasets, and performance metrics used in automated skin cancer detection. Chapter 3 details the research methodology employed in developing the machine learning algorithm for automated skin cancer detection. The methodology includes data collection, preprocessing, feature extraction, model selection, training, validation, and testing. Eight key components are discussed in this chapter, providing a detailed insight into the experimental setup and procedures. Chapter 4 presents an elaborate discussion of the findings obtained from the evaluation of the developed machine learning algorithm. The performance of the algorithm is evaluated on a comprehensive dataset of skin cancer images, and the results are analyzed in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve. The strengths and limitations of the algorithm are discussed, along with potential areas for improvement. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research, and suggesting future directions for further research and development. The significance of the developed machine learning algorithm for automated skin cancer detection is highlighted, emphasizing its potential to assist dermatologists in early detection and diagnosis, leading to improved patient outcomes. In conclusion, this thesis contributes to the field of dermatology by presenting a novel machine learning algorithm for automated skin cancer detection. The algorithm shows promising results in accurately identifying and classifying skin cancer lesions, demonstrating its potential as a valuable tool for dermatologists in clinical practice. Further research is needed to refine the algorithm and validate its performance in real-world clinical settings.

Thesis Overview