Utilizing Artificial Intelligence for Improved Image Quality in Radiography

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.