Implementation of Artificial Intelligence in Radiography: A Comparative Study on Image Analysis Techniques

Thesis Abstract

Abstract
The field of radiography has witnessed significant advancements with the integration of artificial intelligence (AI) technologies for image analysis. This thesis presents a comprehensive study that explores the implementation of AI in radiography, specifically focusing on image analysis techniques. The primary objective of this research is to compare different AI algorithms and methodologies utilized in radiography to enhance diagnostic accuracy and efficiency. Through a systematic literature review and empirical investigation, this study examines the effectiveness of AI in radiography and its impact on clinical practice. Chapter One provides an introduction to the research topic, discussing the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms related to AI and radiography. Chapter Two presents a detailed literature review, encompassing ten key aspects of AI applications in radiography, including machine learning algorithms, deep learning models, image segmentation techniques, and computer-aided diagnosis systems. Chapter Three outlines the research methodology employed in this study, covering eight essential components such as research design, data collection methods, AI model development, image dataset selection, and evaluation metrics. The methodology aims to provide a robust framework for conducting a comparative analysis of AI algorithms in radiography. Chapter Four presents the findings and discussions derived from the empirical research, highlighting the performance evaluation of different AI techniques in radiography. The analysis includes a comparison of accuracy, sensitivity, specificity, and efficiency metrics to assess the effectiveness of AI-based image analysis methods in clinical settings. Finally, Chapter Five offers a comprehensive conclusion and summary of the research thesis, emphasizing the key findings, contributions, limitations, and future research directions. The study underscores the potential of AI technologies to revolutionize radiography practices by improving diagnostic outcomes, reducing interpretation errors, and enhancing overall patient care. In conclusion, this thesis contributes to the growing body of knowledge on the implementation of AI in radiography, providing insights into the comparative analysis of image analysis techniques. The research outcomes aim to inform healthcare professionals, researchers, and policymakers about the benefits and challenges associated with integrating AI in radiography, paving the way for enhanced diagnostic imaging processes and patient outcomes in the healthcare industry.