Thesis Abstract

Abstract
The field of radiography is continuously evolving with advancements in technology, and the integration of artificial intelligence (AI) has shown promise in enhancing radiographic imaging analysis. This thesis explores the application of AI in radiographic imaging analysis, focusing on its potential to improve accuracy, efficiency, and diagnostic capabilities in medical imaging. Chapter One provides an in-depth introduction to the research topic, discussing the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of key terms. Chapter Two presents a comprehensive literature review, examining existing research on AI in radiographic imaging analysis. The review covers various aspects such as AI algorithms, machine learning techniques, image processing, and the integration of AI in radiology practices. Chapter Three outlines the research methodology, detailing the approach taken to investigate the application of AI in radiographic imaging analysis. This chapter includes sections on research design, data collection methods, AI model development, validation procedures, and ethical considerations. Chapter Four presents a detailed discussion of the findings obtained from the research. The chapter explores the impact of AI on radiographic imaging analysis, highlighting the strengths and limitations of AI algorithms in improving diagnostic accuracy and efficiency in medical imaging. Finally, Chapter Five provides a conclusion and summary of the thesis, summarizing the key findings, implications, and future directions for research in the field of AI in radiographic imaging analysis. Overall, this thesis contributes to the growing body of knowledge on the application of AI in radiography and highlights the potential benefits of integrating AI technology into radiographic imaging analysis to enhance diagnostic capabilities and improve patient outcomes.

Thesis Overview