Thesis Abstract

Abstract
The field of radiography has witnessed significant advancements with the integration of artificial intelligence (AI) technologies for image analysis. This thesis presents a comprehensive study that explores the implementation of AI in radiography, specifically focusing on image analysis techniques. The primary objective of this research is to compare different AI algorithms and methodologies utilized in radiography to enhance diagnostic accuracy and efficiency. Through a systematic literature review and empirical investigation, this study examines the effectiveness of AI in radiography and its impact on clinical practice. Chapter One provides an introduction to the research topic, discussing the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms related to AI and radiography. Chapter Two presents a detailed literature review, encompassing ten key aspects of AI applications in radiography, including machine learning algorithms, deep learning models, image segmentation techniques, and computer-aided diagnosis systems. Chapter Three outlines the research methodology employed in this study, covering eight essential components such as research design, data collection methods, AI model development, image dataset selection, and evaluation metrics. The methodology aims to provide a robust framework for conducting a comparative analysis of AI algorithms in radiography. Chapter Four presents the findings and discussions derived from the empirical research, highlighting the performance evaluation of different AI techniques in radiography. The analysis includes a comparison of accuracy, sensitivity, specificity, and efficiency metrics to assess the effectiveness of AI-based image analysis methods in clinical settings. Finally, Chapter Five offers a comprehensive conclusion and summary of the research thesis, emphasizing the key findings, contributions, limitations, and future research directions. The study underscores the potential of AI technologies to revolutionize radiography practices by improving diagnostic outcomes, reducing interpretation errors, and enhancing overall patient care. In conclusion, this thesis contributes to the growing body of knowledge on the implementation of AI in radiography, providing insights into the comparative analysis of image analysis techniques. The research outcomes aim to inform healthcare professionals, researchers, and policymakers about the benefits and challenges associated with integrating AI in radiography, paving the way for enhanced diagnostic imaging processes and patient outcomes in the healthcare industry.

Thesis Overview

The project titled "Implementation of Artificial Intelligence in Radiography: A Comparative Study on Image Analysis Techniques" aims to explore the integration of artificial intelligence (AI) in the field of radiography to enhance image analysis techniques. This research seeks to investigate how AI technologies can be utilized to improve the accuracy, efficiency, and reliability of radiographic image interpretation, ultimately leading to better patient outcomes. Radiography plays a crucial role in modern healthcare by providing valuable insights into the internal structures of the human body. However, the interpretation of radiographic images can be complex and time-consuming, requiring a high level of expertise from radiologists and healthcare professionals. By harnessing the power of AI, this project aims to streamline the image analysis process and provide radiographers with advanced tools to assist in diagnosis and decision-making. The comparative study aspect of this research involves evaluating different AI-driven image analysis techniques and algorithms to determine their effectiveness in enhancing radiographic interpretation. By comparing various AI models, such as machine learning algorithms, deep learning networks, and computer-aided diagnosis systems, this study aims to identify the most suitable approach for improving the accuracy and efficiency of radiographic image analysis. The research overview will explore the current landscape of AI applications in radiography, highlighting the potential benefits and challenges associated with implementing AI technologies in this field. The project will also delve into the existing image analysis techniques used in radiography and examine how AI can complement and enhance these traditional methods. Furthermore, the research overview will discuss the methodology that will be employed to carry out the comparative study, including data collection, algorithm selection, model training, and performance evaluation. The study will involve the use of real-world radiographic images and datasets to assess the efficacy of different AI techniques in improving image analysis accuracy and efficiency. Overall, this project seeks to contribute to the ongoing advancements in radiography by exploring the integration of AI technologies to enhance image analysis techniques. By conducting a comparative study on various AI-driven approaches, this research aims to provide valuable insights into the potential benefits of implementing AI in radiography and pave the way for improved diagnostic capabilities and patient care in the healthcare industry.