Project Abstract

Abstract
Radiography plays a crucial role in modern healthcare by providing detailed insights through imaging for the diagnosis and monitoring of various health conditions. However, the analysis and interpretation of radiography images can be time-consuming and subjective, leading to potential errors and delays in patient diagnosis and treatment. In recent years, the integration of artificial intelligence (AI) technologies in radiography has shown great promise in enhancing the efficiency and accuracy of image analysis. This research project aims to explore the implementation of AI-based automation in radiography image analysis to improve the diagnosis process and overall healthcare outcomes. The focus will be on developing and evaluating a system that utilizes AI algorithms to assist radiographers and clinicians in interpreting radiography images more effectively. Chapter One provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the research, and definitions of key terms. The chapter sets the stage for understanding the importance of implementing AI-based automation in radiography image analysis. Chapter Two presents a comprehensive literature review that examines existing studies, technologies, and best practices related to AI in radiography image analysis. The review covers topics such as AI algorithms, deep learning models, image segmentation, feature extraction, and diagnostic accuracy to provide a solid foundation for the research. Chapter Three outlines the research methodology, detailing the approach, research design, data collection methods, AI model development, validation techniques, and evaluation criteria. The chapter also discusses ethical considerations, potential biases, and limitations of the study to ensure the research is conducted rigorously and ethically. In Chapter Four, the research findings are presented and discussed in detail. The chapter highlights the effectiveness and efficiency of the developed AI-based automation system in radiography image analysis. The discussion covers key insights, challenges encountered, and future recommendations for further improvement and implementation. Finally, Chapter Five concludes the research project by summarizing the key findings, implications, and contributions to the field of radiography and AI. The chapter also discusses the practical applications, limitations, and future research directions to continue advancing the use of AI in radiography image analysis for efficient diagnosis. Overall, this research project aims to contribute to the ongoing efforts to enhance radiography practices by leveraging AI technologies for automation and efficiency in image analysis. The findings and insights gained from this study have the potential to revolutionize the way radiography is conducted, leading to improved diagnostic accuracy, faster treatment decisions, and better patient outcomes in healthcare settings.

Project Overview