Use of Artificial Intelligence in Skin Cancer Detection and Diagnosis

Thesis Abstract

Abstract
Skin cancer is a prevalent form of cancer worldwide, with early detection crucial for successful treatment outcomes. The advancements in artificial intelligence (AI) have shown promise in improving the accuracy and efficiency of skin cancer detection and diagnosis processes. This thesis explores the utilization of AI in skin cancer detection and diagnosis to enhance healthcare outcomes. Chapter 1 provides an introduction to the topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of terms. The chapter sets the foundation for understanding the importance of AI in skin cancer detection and diagnosis. Chapter 2 comprises a comprehensive literature review that examines existing studies, research, and advancements in AI applications for skin cancer detection and diagnosis. The review covers areas such as machine learning algorithms, image processing techniques, and the integration of AI in dermatology practices. Chapter 3 delves into the research methodology employed in this study. It includes detailed discussions on the research design, data collection methods, AI model development, dataset selection, evaluation metrics, validation techniques, and ethical considerations. The chapter outlines the systematic approach taken to investigate the effectiveness of AI in skin cancer detection. Chapter 4 presents an in-depth discussion of the research findings obtained through the implementation of AI models for skin cancer detection and diagnosis. The chapter analyzes the accuracy, sensitivity, specificity, and performance metrics of the developed AI systems, highlighting their strengths and limitations in real-world clinical settings. Chapter 5 serves as the conclusion and summary of the thesis, consolidating the key findings, implications, and recommendations derived from the study. It discusses the potential impact of AI on improving skin cancer detection rates, reducing diagnostic errors, and enhancing patient outcomes. The chapter also identifies areas for future research and development in the field of AI-driven dermatology. In conclusion, the integration of artificial intelligence in skin cancer detection and diagnosis presents a transformative opportunity to revolutionize healthcare practices. By leveraging AI technologies, healthcare providers can enhance the accuracy, efficiency, and accessibility of skin cancer screenings, ultimately leading to improved patient care and outcomes. This thesis contributes to the growing body of knowledge on AI applications in dermatology and underscores the potential benefits of AI in advancing skin cancer detection and diagnosis processes.