Thesis Abstract

Abstract
Skin cancer is a significant public health concern worldwide, with early detection being crucial for successful treatment outcomes. The advancement of artificial intelligence (AI) technologies has shown promise in improving the accuracy and efficiency of skin cancer detection and diagnosis in dermatology. This thesis explores the utilization of AI techniques for skin cancer detection and diagnosis, aiming to enhance the capabilities of healthcare professionals in identifying and treating skin cancer cases promptly. The thesis begins with a comprehensive introduction discussing the background of the study, the problem statement, objectives, limitations, scope, significance, and structure of the thesis. The definitions of key terms related to AI, skin cancer, and dermatology are also provided to establish a solid foundation for the study. Chapter Two presents a detailed literature review covering ten key areas related to AI applications in skin cancer detection and diagnosis. The review examines existing studies, methodologies, algorithms, and technologies used in AI-based skin cancer detection systems, highlighting their strengths, weaknesses, and potential for future improvements. Chapter Three outlines the research methodology employed in this study, including data collection, preprocessing, feature selection, model development, and evaluation metrics. Eight key components of the research methodology are discussed to provide a clear understanding of the experimental design and implementation process. In Chapter Four, the findings of the study are extensively discussed, presenting the results of AI models developed for skin cancer detection and diagnosis. The performance metrics, including sensitivity, specificity, accuracy, and area under the curve, are analyzed to evaluate the efficacy of the AI-based system in comparison to traditional diagnostic methods. Finally, Chapter Five offers a conclusion and summary of the thesis, highlighting the key findings, contributions, limitations, and future research directions. The study concludes that AI technologies have the potential to revolutionize skin cancer detection and diagnosis in dermatology by enhancing accuracy, efficiency, and accessibility to healthcare services. In conclusion, this thesis contributes to the growing body of knowledge on the application of AI for skin cancer detection and diagnosis in dermatology. The findings of this study have implications for healthcare professionals, researchers, and policymakers seeking to improve skin cancer outcomes through the integration of innovative technologies. By harnessing the power of AI, the healthcare industry can advance towards more effective strategies for early detection and treatment of skin cancer, ultimately saving lives and improving patient care.

Thesis Overview

The research project titled "Use of Artificial Intelligence for Skin Cancer Detection and Diagnosis in Dermatology" aims to investigate the potential applications of artificial intelligence (AI) in enhancing the detection and diagnosis of skin cancer. Skin cancer is a prevalent and potentially life-threatening condition that requires early detection for effective treatment. Traditional methods of diagnosing skin cancer rely heavily on visual inspection by dermatologists, which can be time-consuming and prone to human error. The integration of AI technologies, such as machine learning algorithms and computer vision systems, presents a promising opportunity to improve the accuracy and efficiency of skin cancer detection and diagnosis. By leveraging large datasets of skin images and patient information, AI systems can be trained to recognize patterns and abnormalities indicative of skin cancer with high precision. These systems have the capacity to analyze images at a rapid pace, potentially reducing the time taken for diagnosis and enabling early intervention. The research will involve a comprehensive review of existing literature on the use of AI in dermatology, focusing specifically on its applications in skin cancer detection. This review will provide insights into the current state of the field, identify gaps in knowledge, and highlight areas for further research. By synthesizing findings from previous studies, the research aims to build upon existing knowledge and contribute to the advancement of AI-driven solutions in dermatology. In addition to the literature review, the research will also include the development and testing of a prototype AI system for skin cancer detection and diagnosis. This will involve collecting a dataset of skin images, training the AI model using machine learning techniques, and evaluating its performance in accurately identifying skin cancer lesions. The research will assess the sensitivity, specificity, and overall effectiveness of the AI system compared to traditional diagnostic methods. Overall, the research project seeks to demonstrate the potential of AI in revolutionizing the field of dermatology by offering innovative solutions for skin cancer detection and diagnosis. By harnessing the power of AI technologies, dermatologists may be able to improve diagnostic accuracy, reduce unnecessary biopsies, and ultimately enhance patient outcomes in the management of skin cancer.