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Introduction

Breast cancer is one of the most common types of cancer that affects women worldwide. Early detection and accurate diagnosis are crucial in improving the prognosis and increasing the survival rate of breast cancer patients. Mammography is the gold standard for breast cancer screening, but the interpretation of mammograms can be challenging and error-prone due to the subjective nature of human interpretation. Automated analysis of mammograms using computer-aided detection (CAD) systems has emerged as a promising approach to improve the accuracy and efficiency of breast cancer detection.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Overview of Breast Cancer
2.2 Mammography and Breast Cancer Screening
2.3 Computer-aided Detection (CAD) Systems
2.4 Automated Analysis of Mammograms
2.5 Challenges and Limitations of CAD Systems
2.6 Advances in CAD Technology
2.7 Comparative Studies of CAD Systems
2.8 Role of Machine Learning in CAD Systems
2.9 Integration of CAD Systems in Clinical Practice
2.10 Future Directions in CAD Systems

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Preprocessing of Mammograms
3.4 Feature Extraction
3.5 Machine Learning Algorithms
3.6 Performance Evaluation Metrics
3.7 Validation and Testing
3.8 Ethical Considerations

Chapter 4: Discussion of Findings
4.1 Analysis of Study Results
4.2 Comparison with Existing Literature
4.3 Interpretation of Findings
4.4 Implications for Breast Cancer Detection
4.5 Recommendations for Future Research
4.6 Limitations of the Study

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Practical Implications
5.4 Conclusion
5.5 Future Research Directions

Thesis Overview: Automated Analysis of Mammograms for Breast Cancer Detection

Breast cancer is a significant public health concern that affects millions of women globally. Early detection and accurate diagnosis are critical in improving patient outcomes and reducing mortality rates. Mammography has been the primary tool for breast cancer screening, but the interpretation of mammograms can vary depending on the radiologist’s expertise and experience, leading to missed or false-positive diagnoses. Computer-aided detection (CAD) systems have been developed to assist radiologists in interpreting mammograms more accurately and efficiently.

This thesis focuses on the automated analysis of mammograms for breast cancer detection using CAD systems. The research aims to explore the current state of CAD technology, evaluate its effectiveness in detecting breast cancer, and identify challenges and opportunities for improving CAD systems’ performance. The study will involve a comprehensive literature review, data collection and preprocessing, feature extraction, machine learning algorithms, and performance evaluation metrics.

By synthesizing existing research and contributing new insights into the field of automated mammogram analysis, this thesis seeks to enhance the understanding of how CAD systems can improve breast cancer detection rates and assist healthcare providers in making more informed decisions. The findings of this study will have implications for clinical practice, research, and policy development in breast cancer screening and diagnosis.

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