Thesis Abstract

Abstract
The utilization of Artificial Intelligence (AI) in the field of radiography has gained significant attention in recent years due to its potential to enhance diagnostic accuracy, efficiency, and patient care outcomes. This thesis explores the application of AI in radiography image analysis, focusing on its impact on the interpretation of medical images and its implications for radiology practice. The study aims to investigate the effectiveness of AI algorithms in assisting radiographers and radiologists in the interpretation of radiographic images, with a specific emphasis on the detection and classification of abnormalities. Chapter One provides an introduction to the research topic, highlighting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The literature review presented in Chapter Two examines existing research on AI applications in radiography image analysis, including the development of AI algorithms, their performance in image interpretation, and their integration into clinical practice. Chapter Three outlines the research methodology employed in this study, which includes the selection of AI models, data collection and preprocessing, training and testing procedures, and evaluation metrics. The methodology also covers ethical considerations related to the use of AI in radiography and the potential challenges associated with its implementation. Chapter Four presents a detailed discussion of the findings obtained from the application of AI algorithms in radiography image analysis. This chapter analyzes the performance of AI models in detecting various abnormalities in radiographic images, compares their results with human experts, and discusses the implications of AI-assisted image interpretation for radiology practice. Finally, Chapter Five concludes the thesis by summarizing the key findings, discussing the implications of the research outcomes, and providing recommendations for future research and clinical implementation of AI in radiography image analysis. The study contributes to the growing body of knowledge on the potential impact of AI on radiology practice and highlights the importance of collaboration between AI technologies and healthcare professionals to improve patient care outcomes. Overall, this thesis underscores the transformative potential of AI in radiography image analysis and emphasizes the need for further research and development to harness the full capabilities of AI technologies in improving diagnostic accuracy and patient care in radiology practice.

Thesis Overview

The project titled "Application of Artificial Intelligence in Radiography Image Analysis" is a comprehensive study that explores the integration of artificial intelligence (AI) techniques in the field of radiography. Radiography plays a crucial role in the medical field by providing detailed images of the internal structures of the human body, aiding in the diagnosis and treatment of various medical conditions. By leveraging AI technologies, such as machine learning and deep learning algorithms, the project aims to enhance the efficiency and accuracy of image analysis in radiography. The research will begin with an introduction that sets the stage for the study, followed by a background section that provides context on the use of AI in healthcare and radiography. The problem statement will identify the challenges and limitations faced in traditional radiography image analysis methods, highlighting the need for innovative solutions. The objectives of the study will outline the specific goals and outcomes that the research aims to achieve, focusing on improving diagnostic accuracy, reducing processing time, and enhancing overall patient care. The limitations of the study will also be addressed to provide a clear understanding of the boundaries and constraints of the research. The scope of the study will define the specific areas and applications within radiography that will be explored, such as the detection of abnormalities, image enhancement, and automated report generation. The significance of the study will emphasize the potential impact of integrating AI technologies in radiography, including improved diagnostic outcomes, reduced healthcare costs, and enhanced patient experiences. The structure of the thesis will outline the organization of the research work, detailing the chapters and sections that will be included in the study. Additionally, a section on the definition of terms will provide clarity on key concepts and terminology used throughout the project. Chapter two will consist of a comprehensive literature review that examines existing research and developments in the field of AI and radiography image analysis. This section will provide a critical analysis of relevant studies, technologies, and methodologies, highlighting the gaps and opportunities for further research. Chapter three will focus on the research methodology, detailing the approach, tools, and techniques that will be used to conduct the study. This chapter will include sections on data collection, data preprocessing, algorithm selection, model training, and evaluation methods. Chapter four will present the findings of the research, showcasing the results, analyses, and interpretations of the experiments and studies conducted. This section will highlight the effectiveness and performance of the AI-based image analysis techniques in radiography. Finally, chapter five will provide a conclusion and summary of the project thesis, summarizing the key findings, implications, and recommendations for future research. The research overview aims to provide a comprehensive understanding of the project and its significance in advancing the field of radiography through the application of artificial intelligence techniques.