Thesis Abstract

Abstract
This thesis explores the application of artificial intelligence (AI) techniques to enhance image analysis in the field of radiography. The integration of AI technologies has the potential to revolutionize the way radiographic images are interpreted, leading to more accurate diagnoses and improved patient outcomes. The study begins with a comprehensive review of the existing literature on AI in radiography, highlighting key developments and challenges in the field. The research methodology section outlines the approach taken to develop and evaluate AI algorithms for image analysis, including data collection, preprocessing, and model training. The findings of the study reveal the effectiveness of AI-based image analysis in improving the accuracy and efficiency of radiographic interpretation. By leveraging advanced machine learning algorithms, the developed models demonstrate promising results in detecting anomalies, identifying patterns, and assisting radiographers in making informed decisions. The discussion section delves into the implications of these findings for the practice of radiography, emphasizing the potential benefits and limitations of AI technologies in clinical settings. In conclusion, this thesis underscores the significance of utilizing artificial intelligence for enhanced image analysis in radiography. By harnessing the power of AI, radiographers can augment their diagnostic capabilities, streamline workflow processes, and ultimately deliver better patient care. The study contributes to the growing body of research on AI applications in healthcare, paving the way for future advancements in the field of radiography.

Thesis Overview

The project titled "Utilizing Artificial Intelligence for Improved Image Analysis in Radiography" aims to explore the integration of artificial intelligence (AI) technologies in the field of radiography to enhance the analysis of medical images. Radiography plays a crucial role in medical diagnosis and treatment planning, and the accurate interpretation of radiographic images is essential for providing effective healthcare services. However, the manual analysis of radiographic images can be time-consuming, subjective, and prone to errors. By leveraging AI algorithms and machine learning techniques, this research seeks to develop a system that can automatically analyze and interpret radiographic images with high accuracy and efficiency. The use of AI in radiography has the potential to improve diagnostic accuracy, reduce interpretation time, and enhance overall patient care. The research will involve a comprehensive review of existing literature on AI applications in radiography, focusing on image analysis techniques, machine learning algorithms, and their effectiveness in medical imaging. The study will also explore the challenges and limitations associated with the implementation of AI in radiography, such as data security, ethical considerations, and regulatory compliance. Furthermore, the research methodology will involve the design and development of a prototype AI system for image analysis in radiography. The system will be trained using a dataset of radiographic images to enable it to recognize patterns, detect abnormalities, and provide diagnostic insights. The performance of the AI system will be evaluated based on metrics such as sensitivity, specificity, and accuracy compared to traditional manual analysis methods. The findings of this research are expected to contribute to the advancement of AI technologies in radiography and have implications for improving healthcare outcomes. By harnessing the power of AI for image analysis, healthcare professionals can make more informed decisions, leading to better patient outcomes and enhanced clinical practice.