Application of Artificial Intelligence for Skin Cancer Classification in Dermatology

Thesis Abstract

Abstract
Skin cancer is a significant public health concern worldwide, with early detection being crucial for successful treatment outcomes. The use of artificial intelligence (AI) in dermatology has shown great promise in improving the accuracy and efficiency of skin cancer classification. This research explores the application of AI for skin cancer classification in dermatology, aiming to develop a robust system that can assist healthcare professionals in diagnosing skin cancer accurately and efficiently. The thesis begins with an introduction that provides an overview of the research topic, followed by a background of the study that discusses the current challenges in skin cancer diagnosis and the potential benefits of using AI technology. The problem statement highlights the need for more advanced tools to improve skin cancer classification accuracy, leading to the objective of the study, which is to develop an AI-based system for skin cancer classification. The limitations of the study are also discussed, along with the scope of the research, which focuses on utilizing AI algorithms for skin cancer classification. The significance of the study is emphasized, highlighting the potential impact of developing an accurate and efficient AI system for diagnosing skin cancer. The structure of the thesis outlines the organization of the research work, providing a roadmap for the reader to navigate through the chapters. The literature review in Chapter Two explores existing research on AI applications in dermatology and skin cancer classification. Ten key studies are analyzed, highlighting the different AI algorithms and techniques used in skin cancer diagnosis. Chapter Three details the research methodology, including data collection, preprocessing, feature selection, and model development. The chapter also discusses the evaluation metrics and validation techniques used to assess the performance of the AI system. Chapter Four presents the findings of the study, showcasing the results of the developed AI system in classifying skin cancer accurately. The discussion delves into the strengths and limitations of the AI model, comparing its performance with existing methods. Chapter Five concludes the thesis by summarizing the key findings and contributions of the research. The implications of the study for dermatology practice and future research directions are also discussed. In conclusion, the application of artificial intelligence for skin cancer classification in dermatology has the potential to revolutionize the field of dermatology by providing healthcare professionals with a powerful tool for accurate and efficient diagnosis. This research contributes to advancing the knowledge and technology in dermatology, paving the way for improved patient outcomes and better healthcare services.