Thesis Abstract

Abstract
Skin cancer is a prevalent and potentially deadly disease that requires early detection for effective treatment. Traditional diagnosis methods rely heavily on visual inspection by dermatologists, which can be subjective and prone to human error. The advancement of technology has paved the way for the development of computer-aided diagnosis systems to assist in the early detection of skin cancer. This thesis presents the research and development of a Computer-Aided Diagnosis (CAD) system specifically designed for the detection of skin cancer. The system utilizes artificial intelligence algorithms to analyze digital images of skin lesions and provide automated diagnostic suggestions. The primary objective of this research is to improve the accuracy and efficiency of skin cancer diagnosis through the implementation of advanced technology. Chapter one provides an introduction to the research topic, discussing the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter two presents a comprehensive literature review covering ten key aspects related to skin cancer diagnosis, including current trends, challenges, and existing CAD systems. Chapter three outlines the research methodology employed in this study, detailing the data collection process, image processing techniques, feature extraction methods, and the implementation of machine learning algorithms. Eight key components are discussed in this chapter to provide a clear understanding of the research methodology. Chapter four presents an in-depth discussion of the findings obtained from the development and testing of the CAD system. The results are analyzed, and the performance of the system is evaluated in comparison to traditional diagnostic methods. The chapter also discusses the implications of the findings and potential areas for future research and improvement. In conclusion, this thesis summarizes the key findings of the research and highlights the significance of developing a Computer-Aided Diagnosis System for skin cancer detection. The CAD system shows promising results in improving the accuracy and efficiency of skin cancer diagnosis, demonstrating the potential to revolutionize the field of dermatology. Further research and development in this area are essential to enhance the capabilities of CAD systems and facilitate early detection and treatment of skin cancer.

Thesis Overview

The project titled "Development of a Computer-Aided Diagnosis System for Skin Cancer Detection" aims to address the need for efficient and accurate diagnosis of skin cancer through the development of a computer-aided system. Skin cancer is one of the most common types of cancer globally, and early detection plays a crucial role in improving patient outcomes. However, manual diagnosis by dermatologists can be time-consuming and subjective, leading to variations in accuracy and reliability. This research project proposes the development of a computer-aided diagnosis system that leverages machine learning and image analysis techniques to assist dermatologists in diagnosing skin cancer more effectively. By utilizing a vast dataset of skin images, the system will be trained to identify patterns and features indicative of different types of skin cancer, enabling it to provide rapid and accurate assessments. The project will involve various stages, including data collection and preprocessing, feature extraction, model training, and system evaluation. Advanced machine learning algorithms, such as convolutional neural networks (CNNs) and deep learning techniques, will be employed to analyze and classify skin images based on specific characteristics associated with different types of skin cancer. The research will also focus on developing a user-friendly interface that allows dermatologists to interact with the system seamlessly. The interface will provide detailed diagnostic results, including the probability of malignancy, recommendations for further tests or biopsies, and visual explanations of the key features contributing to the diagnosis. By developing a computer-aided diagnosis system for skin cancer detection, this project aims to enhance the accuracy, efficiency, and accessibility of skin cancer diagnosis. The proposed system has the potential to assist healthcare professionals in making more informed decisions, reducing diagnostic errors, and ultimately improving patient outcomes in the field of dermatology.