Thesis Abstract

Abstract
The advancement of artificial intelligence (AI) technology has revolutionized various industries, including healthcare. In the field of radiography, AI has shown promising potential in enhancing the accuracy and efficiency of medical image analysis for early disease detection. This thesis explores the application of AI in radiographic image analysis for early disease detection, focusing on its benefits, limitations, and implications for healthcare practice. Chapter 1 provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of key terms. The chapter sets the foundation for understanding the relevance and importance of applying AI in radiographic image analysis for early disease detection. Chapter 2 conducts a comprehensive literature review on ten key aspects related to the use of AI in radiographic image analysis. The review covers existing studies, methodologies, technologies, and challenges in the field, providing valuable insights into the current state of research and identifying gaps for further exploration. Chapter 3 elucidates the research methodology employed in this study, detailing the research design, data collection methods, AI algorithms utilized, evaluation metrics, and ethical considerations. The chapter outlines the systematic approach undertaken to investigate the application of AI in radiographic image analysis for early disease detection. Chapter 4 presents a detailed discussion of the findings obtained from the research, analyzing the effectiveness of AI algorithms in detecting early signs of diseases in radiographic images. The chapter evaluates the performance, accuracy, and reliability of AI-based systems compared to traditional methods, highlighting the strengths and limitations of AI technology in healthcare applications. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research outcomes, and offering recommendations for future studies and practical implementations. The conclusion underscores the potential of AI in revolutionizing radiographic image analysis for early disease detection and emphasizes the importance of continued research and innovation in this evolving field. In conclusion, this thesis provides a comprehensive analysis of the application of artificial intelligence in radiographic image analysis for early disease detection. By leveraging AI technology, healthcare practitioners can enhance diagnostic accuracy, improve patient outcomes, and advance the field of radiography towards more efficient and effective disease detection strategies.

Thesis Overview