The Use of Artificial Intelligence in Image Analysis for Early Detection of Pathologies in Radiography

Thesis Abstract

Abstract
The integration of artificial intelligence (AI) technologies in radiography has revolutionized the field by enabling advanced image analysis for early detection of pathologies. This thesis focuses on exploring the utilization of AI in radiography to improve diagnostic accuracy and efficiency in detecting various pathologies at their early stages. The research investigates the implementation of AI algorithms in analyzing radiographic images to enhance the detection of abnormalities such as tumors, fractures, and other anomalies. Chapter 1 provides an introduction to the research topic, outlining the background of the study, the problem statement, objectives, limitations, scope, significance, and the structure of the thesis. The chapter also defines key terms essential for understanding the research context. Chapter 2 presents a comprehensive literature review that examines existing studies, methodologies, and technologies related to the use of AI in radiography for pathology detection. The review covers ten key areas, including the evolution of AI in radiography, the benefits and challenges of AI implementation, and the current trends and future directions in the field. Chapter 3 details the research methodology employed in this study, encompassing the research design, data collection methods, AI algorithms utilized, image analysis techniques, validation processes, and ethical considerations. The chapter also discusses the sample population, data preparation procedures, and statistical analysis methods applied. In Chapter 4, the findings of the research are extensively discussed, highlighting the effectiveness of AI in enhancing the early detection of pathologies in radiography. The chapter presents detailed analyses of the results obtained from the application of AI algorithms to radiographic images, demonstrating the improved accuracy and efficiency of pathology detection compared to traditional methods. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research outcomes, and providing recommendations for future studies and practical implementations. The conclusion emphasizes the significance of integrating AI technologies in radiography for early pathology detection and outlines potential areas for further research and development in the field. Overall, this thesis contributes to the advancement of radiography by showcasing the potential of AI in improving diagnostic capabilities and facilitating early detection of pathologies. The research underscores the importance of leveraging AI technologies to enhance the accuracy, efficiency, and effectiveness of radiographic image analysis, ultimately leading to better patient outcomes and healthcare practices.