Project Abstract

Abstract
This research project focuses on the implementation of Artificial Intelligence (AI) for image analysis in the field of Radiography. Radiography plays a crucial role in the diagnosis and treatment of various medical conditions by producing images of the internal structures of the human body. The integration of AI technology into radiography has the potential to revolutionize the way medical images are interpreted, leading to more accurate diagnoses and improved patient outcomes. The research begins with an introduction to the topic, providing background information on the use of AI in healthcare and the significance of applying AI to radiography. The problem statement highlights the challenges faced in traditional image analysis methods and the need for more advanced technologies to enhance the accuracy and efficiency of diagnostic processes. The objectives of the study are to explore the potential applications of AI in radiography, assess the benefits and limitations of AI-based image analysis systems, and evaluate the impact of AI on radiographic interpretation. The scope of the research is defined to focus on the implementation of AI algorithms for image analysis in radiography settings, particularly in the context of diagnostic imaging. A comprehensive literature review is conducted to examine existing studies and technologies related to AI in radiography. The review covers topics such as machine learning algorithms, deep learning models, image recognition techniques, and the integration of AI into medical imaging systems. The findings from the literature review inform the research methodology, guiding the selection of appropriate AI tools and techniques for image analysis in radiography. The research methodology outlines the process of data collection, image acquisition, algorithm development, and model training for implementing AI in radiographic image analysis. Various aspects of the methodology, including dataset preparation, feature extraction, model evaluation, and performance metrics, are discussed in detail to ensure the accuracy and reliability of the AI-based system. In the discussion of findings, the research presents the results of applying AI algorithms to radiographic images and evaluates the performance of the AI-based image analysis system. The findings demonstrate the effectiveness of AI in enhancing the accuracy of radiographic interpretation, reducing diagnostic errors, and improving the efficiency of image analysis processes. In conclusion, the study highlights the significant contributions of AI to radiography and emphasizes the potential benefits of integrating AI technology into clinical practice. The research findings support the adoption of AI-based image analysis systems in radiography to enhance diagnostic accuracy, improve patient care, and advance medical imaging technologies. Keywords Artificial Intelligence, Image Analysis, Radiography, Machine Learning, Deep Learning, Diagnostic Imaging, Healthcare Technology.

Project Overview