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Development of a Computer-Aided Detection System for Early Diagnosis of Breast Cancer using Mammography Images

 

Table Of Contents


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 Review of Radiography in Breast Cancer Detection
2.2 Computer-Aided Detection Systems in Medical Imaging
2.3 Importance of Early Diagnosis in Breast Cancer
2.4 Previous Studies on Mammography Image Analysis
2.5 Machine Learning in Medical Image Processing
2.6 Challenges in Breast Cancer Detection
2.7 Advances in Radiography Technology
2.8 Role of Radiographers in Breast Cancer Screening
2.9 Ethical Considerations in Medical Imaging Research
2.10 Future Trends in Computer-Aided Diagnosis Systems

Chapter 3

: Research Methodology 3.1 Research Design
3.2 Data Collection Methods
3.3 Sampling Techniques
3.4 Data Analysis Procedures
3.5 Software and Tools Used
3.6 Ethical Considerations
3.7 Pilot Study Details
3.8 Validation of the Computer-Aided Detection System

Chapter 4

: Discussion of Findings 4.1 Analysis of Mammography Images
4.2 Performance Evaluation of the Detection System
4.3 Comparison with Existing Methods
4.4 Interpretation of Results
4.5 Impact of Findings on Breast Cancer Diagnosis

Chapter 5

: Conclusion and Summary 5.1 Summary of Research Findings
5.2 Conclusion
5.3 Contributions to the Field
5.4 Implications for Practice
5.5 Recommendations for Future Research

Thesis Abstract

Abstract
Breast cancer is a significant health concern worldwide, with early detection being crucial for successful treatment outcomes. Mammography is a widely used screening tool for breast cancer, but the interpretation of mammograms can be challenging and subjective. This research project focuses on the development of a Computer-Aided Detection (CAD) system to assist radiographers and clinicians in the early diagnosis of breast cancer using mammography images. The aim is to improve the accuracy and efficiency of breast cancer detection, ultimately leading to improved patient outcomes. The study begins with a comprehensive review of the existing literature in Chapter Two, examining the current state-of-the-art in CAD systems for breast cancer detection and highlighting the gaps in knowledge that this research aims to address. Chapter Three outlines the research methodology employed, detailing the data collection process, image processing techniques, algorithm development, and validation methods used to train and test the CAD system. In Chapter Four, the findings of the study are presented and discussed in detail. The performance of the developed CAD system is evaluated in terms of sensitivity, specificity, and overall accuracy compared to manual interpretation by radiologists. The strengths and limitations of the system are analyzed, along with potential areas for improvement and future research directions. Lastly, Chapter Five provides a summary of the project, drawing conclusions based on the research outcomes and discussing the implications for clinical practice. The significance of the developed CAD system in improving early detection rates and reducing false positives in breast cancer screening is highlighted. The study contributes to the ongoing efforts to enhance the efficiency and accuracy of breast cancer diagnosis, ultimately benefiting patients, healthcare providers, and the broader community. In conclusion, the "Development of a Computer-Aided Detection System for Early Diagnosis of Breast Cancer using Mammography Images" project represents a significant step forward in the field of radiography and breast cancer detection. By leveraging advanced technology and image analysis techniques, this research has the potential to make a meaningful impact on the early detection and management of breast cancer, ultimately improving patient outcomes and advancing the field of medical imaging.

Thesis Overview

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