Thesis Abstract

Abstract
This thesis explores the integration of artificial intelligence (AI) in image analysis to enhance radiography diagnostics, aiming to improve the accuracy and efficiency of medical imaging interpretation. The implementation of AI technologies in radiography has gained significant attention due to its potential to revolutionize the field by providing automated analysis, aiding in earlier and more accurate disease detection. The research focuses on developing a system that utilizes AI algorithms to analyze radiographic images for various medical conditions, such as bone fractures, tumors, and abnormalities. The study begins with a comprehensive review of the existing literature on AI applications in radiography diagnostics, presenting an overview of the current state-of-the-art technologies, methodologies, and challenges in the field. The literature review highlights the potential benefits of AI in enhancing diagnostic accuracy, reducing interpretation time, and improving patient outcomes. Following the literature review, the research methodology section outlines the approach taken to design and develop the AI-based image analysis system. This includes data collection, preprocessing, feature extraction, algorithm selection, model training, and validation processes. The methodology section also discusses the evaluation metrics used to assess the performance of the AI system in comparison to traditional radiography interpretation methods. The findings of the study are presented in the discussion section, where the performance of the AI system in detecting and diagnosing various medical conditions is evaluated. The results demonstrate the effectiveness of AI in improving the accuracy and efficiency of radiography diagnostics, showcasing its potential to assist radiologists in making more informed decisions and providing better patient care. In conclusion, the thesis summarizes the key findings and contributions of the research, emphasizing the significance of integrating AI in radiography diagnostics to enhance healthcare outcomes. The study underscores the importance of continued research and development in this area to further advance the capabilities of AI technologies in medical imaging interpretation. Overall, this thesis contributes to the growing body of knowledge on the utilization of artificial intelligence in image analysis for improved radiography diagnostics, offering insights into the potential benefits and challenges of implementing AI technologies in healthcare settings.

Thesis Overview