Thesis Abstract

Abstract
Skin cancer is one of the most common forms of cancer worldwide, with early detection being crucial for successful treatment outcomes. The development of a Computer-Aided Diagnosis (CAD) system for skin cancer detection aims to improve the accuracy and efficiency of diagnosing skin lesions. This thesis presents a comprehensive study on the design, development, and evaluation of a CAD system specifically tailored for skin cancer detection. The thesis begins with an introduction to the problem statement, highlighting the importance of early detection in improving patient outcomes. A detailed background of the study is provided, exploring the current methods of skin cancer diagnosis and the limitations of existing approaches. The objectives of the study are outlined, focusing on the development of a CAD system that can assist dermatologists in accurately identifying and classifying skin lesions. The methodology chapter describes the research approach taken to design and implement the CAD system. Various machine learning algorithms and image processing techniques are explored for feature extraction and classification of skin lesions. The dataset used for training and testing the CAD system is described, along with the evaluation metrics employed to assess its performance. The literature review chapter provides a comprehensive overview of existing research in the field of computer-aided diagnosis for skin cancer detection. Ten key studies are analyzed, highlighting the advancements and challenges in developing CAD systems for dermatological applications. Chapter four presents a detailed discussion of the findings obtained from the evaluation of the CAD system. The performance metrics, including sensitivity, specificity, and accuracy, are reported, demonstrating the effectiveness of the proposed system in detecting and classifying skin lesions. The limitations of the study are discussed, along with recommendations for future research and improvements to the CAD system. Finally, the conclusion chapter summarizes the key findings of the thesis and provides insights into the significance of the research. The contributions of the study to the field of dermatology and computer-aided diagnosis are highlighted, emphasizing the potential impact of the developed CAD system on clinical practice. Overall, this thesis contributes to the advancement of skin cancer detection through the development of an accurate and efficient Computer-Aided Diagnosis system.

Thesis Overview

The project titled "Development of a Computer-Aided Diagnosis System for Skin Cancer Detection" aims to address the critical need for accurate and efficient skin cancer detection through the implementation of advanced technological solutions. Skin cancer is one of the most common types of cancer globally, with early detection being crucial for successful treatment outcomes. Traditional methods of skin cancer diagnosis rely heavily on visual inspection by dermatologists, which can be subjective and time-consuming. The proposed computer-aided diagnosis system leverages cutting-edge technologies such as artificial intelligence and image processing algorithms to enhance the accuracy and efficiency of skin cancer detection. By analyzing digital images of skin lesions, the system will be able to identify potential malignancies with high precision, aiding healthcare professionals in making timely and informed decisions regarding patient care. This research project will involve the development and testing of the computer-aided diagnosis system using a diverse dataset of skin lesion images. The system will be trained using machine learning techniques to recognize patterns and features associated with different types of skin cancer. Through rigorous validation and testing procedures, the performance of the system will be evaluated in terms of sensitivity, specificity, and overall diagnostic accuracy. The significance of this research lies in its potential to revolutionize the field of dermatology by providing a reliable tool for early skin cancer detection. By automating the process of analyzing skin lesions, the computer-aided diagnosis system can assist healthcare providers in improving diagnostic accuracy, reducing unnecessary biopsies, and ultimately saving lives through early intervention. Overall, this project represents a critical step towards enhancing the capabilities of skin cancer diagnosis through the integration of technology and medical expertise. The development of a computer-aided diagnosis system for skin cancer detection has the potential to transform healthcare practices and improve patient outcomes in the field of dermatology.