Thesis Abstract

Abstract
The field of radiography has seen significant advancements with the integration of artificial intelligence (AI) technologies, paving the way for automated diagnosis in medical imaging. This thesis explores the application of AI in radiography for the purpose of automated diagnosis, aiming to enhance the efficiency and accuracy of diagnostic processes. The study delves into the utilization of AI algorithms, machine learning techniques, and deep learning models to analyze radiographic images and assist healthcare professionals in detecting, classifying, and diagnosing various medical conditions. The introduction provides an overview of the research, highlighting the background, problem statement, objectives, limitations, scope, significance, and structure of the thesis. The literature review in Chapter Two presents a comprehensive analysis of existing studies, discussing key concepts, methodologies, and findings related to AI in radiography and automated diagnosis. Chapter Three focuses on the research methodology, detailing the research design, data collection methods, AI algorithms employed, model training procedures, and evaluation metrics utilized to assess the performance of the automated diagnostic system. The chapter also addresses ethical considerations and potential biases associated with AI applications in healthcare. In Chapter Four, the discussion of findings section presents the results of experiments conducted to evaluate the effectiveness and reliability of the AI-based automated diagnosis system. The chapter examines the accuracy, sensitivity, specificity, and overall performance of the system in comparison to traditional diagnostic methods, highlighting the strengths and limitations of AI in radiography. Finally, Chapter Five provides a conclusion and summary of the project thesis, outlining the key findings, contributions, implications, and future research directions in the field of AI in radiography for automated diagnosis. The study underscores the potential of AI technologies to revolutionize diagnostic practices in radiology, improving patient outcomes and healthcare delivery. Overall, this research contributes to the growing body of knowledge on the integration of AI in radiography and sets the stage for further advancements in automated diagnosis for enhanced healthcare services.

Thesis Overview