Development of a Computer-Aided Diagnosis System for Skin Cancer Detection

Thesis Abstract

Abstract
Skin cancer is a prevalent disease with increasing incidence globally. Early detection is crucial for successful treatment outcomes. This research project focuses on the development of a Computer-Aided Diagnosis (CAD) system for skin cancer detection to assist healthcare professionals in accurate and timely diagnosis. The CAD system utilizes advanced image processing and machine learning techniques to analyze dermatoscopic images and classify skin lesions as either benign or malignant. The primary objective of this study is to improve the accuracy and efficiency of skin cancer diagnosis, ultimately leading to better patient outcomes. The thesis begins with an introduction outlining the background of the study, the problem statement, research objectives, limitations, scope, significance, and the structure of the thesis. A comprehensive literature review in Chapter Two explores existing research on skin cancer diagnosis, CAD systems, image processing techniques, and machine learning algorithms. Chapter Three details the research methodology, including data collection, preprocessing, feature extraction, model development, and evaluation metrics. Chapter Four presents the findings of the study, including the performance evaluation of the CAD system in terms of sensitivity, specificity, accuracy, and computational efficiency. The discussion section critically analyzes the results, highlights the strengths and limitations of the CAD system, and provides insights for future research and improvements. Chapter Five concludes the thesis by summarizing the key findings, discussing the implications for clinical practice, and suggesting areas for further research. Overall, this research contributes to the field of dermatology by proposing a novel CAD system for skin cancer detection that shows promising results in terms of accuracy and efficiency. The development of such a system has the potential to revolutionize skin cancer diagnosis, leading to early detection, improved patient care, and better treatment outcomes.