The Use of Artificial Intelligence in Radiographic Image Analysis for Early Detection of Pathologies

Thesis Abstract

Abstract
This thesis investigates the application of artificial intelligence (AI) in radiographic image analysis for the early detection of pathologies. The use of AI technologies has shown promising results in various fields, and its potential in radiography holds great significance for improving healthcare outcomes. The study aims to address the limitations of traditional radiographic image analysis methods by leveraging AI algorithms to enhance diagnostic accuracy and speed in detecting pathologies at an early stage. Chapter One provides an introduction to the research topic, offering a background of the study, defining the problem statement, objectives, limitations, scope, significance, and the structure of the thesis. Chapter Two presents a comprehensive literature review that covers ten key aspects related to AI in radiographic image analysis, highlighting existing studies, methodologies, and advancements in the field. Chapter Three details the research methodology employed in this study, including data collection methods, AI algorithms used, image processing techniques, validation procedures, and performance evaluation metrics. The chapter also discusses ethical considerations and limitations of the methodology. Chapter Four presents an in-depth discussion of the findings obtained from the application of AI in radiographic image analysis. The chapter explores the effectiveness of AI algorithms in detecting various pathologies, compares the results with traditional methods, and discusses the implications for clinical practice. Finally, Chapter Five offers a conclusion and summary of the thesis, highlighting the key findings, contributions to the field, implications for future research, and recommendations for healthcare practitioners. The study concludes that the integration of AI in radiographic image analysis has the potential to revolutionize diagnostic processes, leading to earlier detection and improved treatment outcomes for patients with pathologies.