Thesis Abstract

Abstract
This thesis investigates the utilization of artificial intelligence (AI) techniques to enhance image quality in radiography. Radiography plays a crucial role in modern healthcare by providing detailed images to aid in diagnosis and treatment planning. However, the quality of radiographic images can be affected by various factors, such as equipment limitations, patient movement, and suboptimal imaging techniques. The integration of AI algorithms into radiography systems has the potential to address these challenges and improve the overall quality of radiographic images. The study begins with an exploration of the background of radiography and the importance of image quality in the diagnostic process. The problem statement highlights the current limitations in image quality and the need for advanced solutions to overcome these challenges. The objectives of the study are to evaluate the effectiveness of AI in enhancing image quality, identify the key factors influencing image quality improvement, and develop a framework for implementing AI algorithms in radiography. The research methodology encompasses a comprehensive literature review of AI applications in radiography and medical imaging. The study investigates various AI techniques, such as deep learning, convolutional neural networks, and image processing algorithms, to enhance image quality. The methodology also includes the collection and analysis of radiographic images to evaluate the performance of AI algorithms in improving image quality. The findings of the study demonstrate the effectiveness of AI in enhancing image quality in radiography. AI algorithms can reduce noise, enhance contrast, and improve image sharpness, leading to more accurate and reliable diagnostic information. The discussion of findings explores the impact of AI on radiography practice, highlighting the potential benefits for healthcare providers and patients. In conclusion, the study emphasizes the significance of integrating AI into radiography systems to improve image quality and enhance diagnostic accuracy. The implementation of AI algorithms offers opportunities to optimize imaging protocols, reduce radiation exposure, and streamline the diagnostic process. The thesis contributes to the advancement of radiography practice by demonstrating the potential of AI to revolutionize image quality enhancement in healthcare. Keywords Radiography, Artificial Intelligence, Image Quality, Deep Learning, Convolutional Neural Networks, Medical Imaging, Diagnostic Accuracy, Healthcare Technology.

Thesis Overview

The project titled "Utilizing Artificial Intelligence for Improved Image Quality in Radiography" aims to explore the potential of artificial intelligence (AI) in enhancing image quality and diagnostic accuracy in radiography. The field of radiography plays a crucial role in modern healthcare by providing detailed images for diagnosis and treatment planning. However, challenges such as image noise, artifacts, and suboptimal quality can impact the accuracy of diagnoses and subsequent patient outcomes. By harnessing the power of AI, this research seeks to address these challenges and improve the overall quality of radiographic images. AI technologies, such as machine learning algorithms and deep learning models, have shown promise in image enhancement, denoising, artifact removal, and feature extraction. These tools have the potential to assist radiographers and radiologists in interpreting images more accurately and efficiently, leading to better clinical decisions and patient care. The research will involve a comprehensive literature review to explore the current state of AI applications in radiography and identify gaps in existing research. By synthesizing and analyzing relevant studies, the project aims to establish a solid foundation for the implementation of AI techniques in image quality improvement. The methodology will involve the development and testing of AI algorithms on radiographic datasets to evaluate their effectiveness in enhancing image quality. The findings of this research are expected to contribute to the advancement of radiography practice by demonstrating the potential benefits of AI in improving image quality and diagnostic accuracy. By leveraging AI technologies, healthcare providers can enhance the quality of patient care, optimize workflow efficiency, and ultimately improve patient outcomes. This research overview underscores the importance of integrating AI into radiography practice to achieve better image quality and enhance the overall quality of healthcare delivery.