Thesis Abstract

Abstract
Skin cancer is one of the most common types of cancer worldwide, with early detection playing a crucial role in successful treatment and patient outcomes. In this thesis, we present the development of a Computer-Aided Diagnosis (CAD) system for skin cancer detection. The CAD system is designed to assist dermatologists in accurately diagnosing skin lesions by analyzing digital images of the skin. The research begins with an introduction to the importance of early detection in skin cancer, highlighting the limitations of current diagnostic methods and the potential benefits of implementing a CAD system. The background of the study provides an overview of skin cancer, its types, causes, risk factors, and prevalence, setting the context for the development of the CAD system. The problem statement identifies the challenges faced by dermatologists in accurately diagnosing skin cancer, such as inter-observer variability and limited access to specialized expertise. The objectives of the study outline the goals of developing the CAD system, including improving diagnostic accuracy, reducing diagnostic time, and enhancing patient outcomes. The limitations of the study are discussed, acknowledging constraints such as the availability of high-quality image datasets and the need for further validation of the CAD system in clinical settings. The scope of the study defines the specific parameters and focus areas of the research, outlining the types of skin lesions and diagnostic techniques included in the CAD system. The significance of the study emphasizes the potential impact of the CAD system on improving skin cancer diagnosis, leading to early detection, timely treatment, and better patient care. The structure of the thesis provides an overview of the organization of the research, highlighting the chapters and sub-sections that constitute the thesis. In the literature review chapter, existing research on CAD systems for skin cancer detection is critically reviewed, focusing on the algorithms, techniques, and performance metrics used in previous studies. The research methodology chapter outlines the data collection process, image analysis techniques, algorithm development, and evaluation methods used in developing the CAD system. The discussion of findings chapter presents the results of testing the CAD system on a dataset of skin lesion images, evaluating its performance in terms of sensitivity, specificity, accuracy, and computational efficiency. The conclusion summarizes the key findings of the research, highlighting the strengths and limitations of the CAD system, and providing recommendations for future research and implementation. In conclusion, the development of a CAD system for skin cancer detection represents a significant advancement in the field of dermatology, offering a promising tool for improving diagnostic accuracy and patient outcomes. The research contributes to the growing body of knowledge on computer-aided diagnosis systems and serves as a foundation for further research and innovation in skin cancer detection.

Thesis Overview