Development and Evaluation of AI-Assisted Image Processing Techniques for Improved Diagnostic Accuracy in Radiography

Thesis Abstract

Abstract
This thesis presents the development and evaluation of AI-assisted image processing techniques aimed at enhancing diagnostic accuracy in radiography. The integration of artificial intelligence (AI) in medical imaging has shown promising results in improving the efficiency and precision of diagnostic procedures. The study focuses on exploring the potential of AI algorithms in enhancing the quality of radiographic images and assisting radiographers in making more accurate diagnoses. The research begins with a comprehensive review of the existing literature on AI applications in radiography. This review covers various aspects such as the development of AI algorithms, their implementation in medical imaging, and the impact of AI on diagnostic accuracy. The literature review also discusses the challenges and limitations associated with AI integration in radiography, providing a foundation for the research methodology. The methodology section outlines the research design, data collection methods, and analysis techniques employed in the study. The research methodology includes the development of AI algorithms for image processing, the selection of radiographic images for evaluation, and the assessment of diagnostic accuracy using both conventional and AI-assisted approaches. The study also incorporates feedback from radiographers and medical professionals to validate the effectiveness of the AI-assisted techniques. The findings of the study are presented in detail in the discussion chapter, highlighting the improvements in diagnostic accuracy achieved through the use of AI-assisted image processing techniques. The results demonstrate the potential of AI algorithms in enhancing the quality of radiographic images, reducing interpretation errors, and improving overall patient outcomes. The discussion chapter also addresses the practical implications of integrating AI into radiography practice, including considerations for implementation and training for healthcare professionals. In conclusion, this thesis provides valuable insights into the development and evaluation of AI-assisted image processing techniques for improving diagnostic accuracy in radiography. The research findings underscore the potential of AI algorithms in revolutionizing the field of medical imaging and enhancing the quality of patient care. The study contributes to the growing body of knowledge on AI applications in healthcare and serves as a foundation for future research in this rapidly evolving field.