Thesis Abstract

Abstract
Skin cancer is one of the most common types of cancer globally, with melanoma being the most aggressive form that can lead to significant morbidity and mortality if not diagnosed and treated early. The development of computer-aided diagnosis (CAD) systems for skin cancer detection has emerged as a promising approach to improve the accuracy and efficiency of diagnosis. This thesis presents the research and development of a CAD system for skin cancer detection, focusing on the integration of advanced imaging technologies, machine learning algorithms, and clinical data to enhance diagnostic capabilities. The thesis begins with a comprehensive introduction that outlines the background of the study, problem statement, research objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. A thorough literature review is presented in Chapter Two, covering ten key areas related to skin cancer diagnosis, CAD systems, imaging techniques, machine learning algorithms, and previous research in this field. Chapter Three details the research methodology employed in developing the CAD system, including data collection, preprocessing, feature extraction, selection of machine learning algorithms, model training, and evaluation metrics. The chapter also discusses the ethical considerations and validation strategies implemented in the study. In Chapter Four, the findings of the study are presented and discussed in detail. The performance of the developed CAD system in terms of sensitivity, specificity, accuracy, and computational efficiency is evaluated using a dataset of skin cancer images. The results are compared with existing methods to demonstrate the effectiveness and potential clinical utility of the proposed system. The thesis concludes with Chapter Five, which provides a summary of the key findings, implications of the research, limitations of the study, and recommendations for future research in the field of CAD systems for skin cancer detection. The significance of the research in improving early detection rates, reducing unnecessary biopsies, and enhancing clinical decision-making is highlighted. Overall, this thesis contributes to the advancement of skin cancer diagnosis by introducing a novel CAD system that leverages cutting-edge technologies and methodologies. The system has the potential to revolutionize the field of dermatology by providing clinicians with a powerful tool for accurate and efficient skin cancer detection, ultimately leading to improved patient outcomes and quality of care.

Thesis Overview

The project titled "Development of a Computer-Aided Diagnosis System for Skin Cancer Detection" aims to address the pressing need for accurate and efficient methods of diagnosing skin cancer. Skin cancer is a prevalent and potentially life-threatening disease that requires early detection for successful treatment. Traditional methods of skin cancer diagnosis rely heavily on visual inspection by dermatologists, which can be subjective and prone to errors. To overcome these limitations, the proposed project seeks to develop a computer-aided diagnosis system that can assist healthcare professionals in accurately identifying and diagnosing skin cancer. The research will focus on leveraging advanced technologies such as artificial intelligence, machine learning, and image processing to analyze dermatoscopic images of skin lesions. By training the system on a large dataset of annotated images, the goal is to enable the system to automatically detect and classify different types of skin lesions, including potentially cancerous ones. This automated approach has the potential to improve diagnostic accuracy, reduce the need for invasive procedures, and enable earlier detection of skin cancer. The project will involve several key steps, including data collection, preprocessing of images, feature extraction, model training, and evaluation. Various machine learning algorithms will be explored and compared to determine the most effective approach for skin cancer detection. The performance of the developed system will be evaluated using metrics such as sensitivity, specificity, and accuracy, with a focus on achieving high levels of sensitivity to ensure early detection of skin cancer cases. Ultimately, the successful development of a computer-aided diagnosis system for skin cancer detection has the potential to revolutionize the field of dermatology by providing a reliable and efficient tool for healthcare professionals. This system could significantly improve the accuracy and timeliness of skin cancer diagnoses, leading to better patient outcomes and potentially saving lives.