Thesis Abstract

Abstract
Breast cancer is one of the most common types of cancer affecting women worldwide, and early detection is crucial for successful treatment outcomes. In recent years, advancements in medical imaging technology have enabled the development of computer-aided diagnosis (CAD) systems to assist radiologists in interpreting mammography images for breast cancer detection. This thesis presents the development and evaluation of a CAD system specifically designed for breast cancer detection in mammography images. Chapter 1 provides an introduction to the research topic, including the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and key definitions. Chapter 2 presents a comprehensive literature review covering ten key aspects related to CAD systems, mammography imaging, breast cancer detection techniques, and existing research in the field. Chapter 3 details the research methodology employed in developing and evaluating the proposed CAD system. This includes the selection of datasets, preprocessing of mammography images, feature extraction techniques, machine learning algorithms used for classification, performance evaluation metrics, and validation methods. Additionally, the chapter discusses ethical considerations and data privacy measures implemented during the study. Chapter 4 presents a detailed discussion of the findings obtained through the development and evaluation of the CAD system. This includes the performance metrics such as sensitivity, specificity, accuracy, and area under the curve (AUC) achieved by the system in detecting breast cancer from mammography images. The chapter also examines the strengths and limitations of the CAD system, as well as potential areas for further improvement. Chapter 5 concludes the thesis by summarizing the key findings, implications of the research, and recommendations for future work. The conclusion highlights the significance of the developed CAD system in assisting radiologists for early breast cancer detection, ultimately contributing to improved patient outcomes and healthcare efficiency. In conclusion, this thesis contributes to the ongoing efforts in enhancing breast cancer detection through the development and evaluation of a CAD system for analyzing mammography images. The findings demonstrate the potential of CAD systems to assist healthcare professionals in diagnosing breast cancer accurately and efficiently. Future research directions may focus on expanding the capabilities of CAD systems, integrating artificial intelligence techniques, and conducting clinical trials for real-world implementation.

Thesis Overview