The study will classify breast cancers into foremost problems: (Benign tumor and Malignant tumor). A benign tumor is a most cancers does now not invade its surrounding tissue or spread around the host. A malignant tumor is another kind of cancers which can invade its surrounding tissue or spread around the frame of the host. Benign cancers on uncommon event can also surely result in someone’s death, but as a fashionable rule they're no longer nearly as horrific because the malignant cancers. The malignant cancers at the contrary are like those killer bees. In this situation, you do not need to be doing something to them or maybe be everywhere near their hive, they will just spread out and attack you emass – they could even kill the individual if they are extreme enough.
Manual manner of cancer category into benign and malignant may be very tedious, susceptible to human error and unnecessarily time consuming. The proposed system while constructed can robotically classify the sort of most cancers into the safe (benign) and also the risky (malignant). This machine plays this role through the usage of machine getting to know algorithm. The following is the extensive of this new system: Classification mistakes could be notably removed, early analysis of disorder, removal of possible human mistakes and the device does no longer die. However, the researcher seeks to detect and assess the class of breast using Machine learning.
Table of Contents
- CHAPTER ONE
- INTRODUCTION
- Background of the study
- Statement of the Problem
- Aim and Objective of the study
- Research Questions
- Significance of study
- Scope of the study
- Limitation of the study
- Operational definition of terms
- CHAPTER TWO
- LITERATURE REVIEW
- Overview of Breast Cancer
- Risk Factors
- Symptoms of breast cancer
- Screening/Diagnosis of breast cancer
- Treatment of breast cancer
- Breast cancer type using system gaining knowledge
- Diagnosis and Treatment of Breast Cancer
- Advantages and Disadvantages of Machine learning
- EMPIRICAL STUDIES
- CHAPTER THREE
- SYSTEM DESIGN AND ANALYSIS
- Data Collection Methods
- DESIGN LANGUAGES, TOOLS, AND TECHNIQUES OF THE PROPOSED SYSTEM
- TECHNIQUES OF PROPOSED SYSTEM
- ANALYSIS OF THE EXISTING SYSTEM
- ANALYSIS OF THE PROPOSED SYSTEM
- DESIGN OF THE PROPOSED SYSTEM
- FUNCTIONALITY OF THE SYSTEM
- CHAPTER FOUR
- IMPLEMENTATION AND DOCUMENTATION
- SYSTEM TESTING
- Test Plan
- Test Data
- Test Result
- CHAPTER FIVE
- SUMMARY, CONCLUSION AND RECOMMENDATION
- Summary
- Conclusion
- Recommendations
- SUGGESTION FOR FUTURE STUDIES
Objectives and Key Themes
The main objective of this study is to develop a machine learning-based system for the classification of breast cancer. The study aims to improve the accuracy and efficiency of breast cancer diagnosis.
- Application of machine learning to breast cancer classification
- Analysis of various machine learning techniques for breast cancer diagnosis
- Development and evaluation of a breast cancer classification system
- Exploration of the advantages and disadvantages of using machine learning for breast cancer diagnosis
- Review of existing literature on breast cancer diagnosis and treatment.
Chapter Summaries
CHAPTER ONE: INTRODUCTION: This chapter introduces the study, providing background information on breast cancer and the challenges associated with its diagnosis. It clearly states the problem the research addresses—the need for more accurate and efficient methods—and outlines the study's aims and objectives. The research questions are defined, along with the significance and scope of the study, its limitations, and operational definitions of key terms. This sets the stage for the subsequent chapters, providing context and justification for the research undertaken.
CHAPTER TWO: LITERATURE REVIEW: This chapter presents a comprehensive overview of existing literature related to breast cancer. It delves into the specifics of breast cancer, including its overview, risk factors, symptoms, screening and diagnostic methods, treatment options, and various types. A crucial aspect is the exploration of different machine learning techniques and their applications in breast cancer diagnosis and treatment, weighing their advantages and disadvantages. The chapter concludes with a review of existing empirical studies relevant to the field, providing a strong foundation for the proposed research.
CHAPTER THREE: SYSTEM DESIGN AND ANALYSIS: This chapter details the design and analysis of the proposed system for breast cancer classification. It describes the data collection methods employed, outlines the design languages, tools, and techniques used in the system's development, and explains the specific techniques incorporated. A key component is the comparative analysis of existing and proposed systems, showcasing the improvements and innovations introduced by the proposed model. The chapter concludes by outlining the functionalities of the developed system.
CHAPTER FOUR: IMPLEMENTATION AND DOCUMENTATION: This chapter focuses on the implementation and testing of the breast cancer classification system. It describes the system testing procedures, including the test plan, the test data used, and the results obtained. This section provides critical evidence of the system's performance and its effectiveness in classifying breast cancer, demonstrating the practical application of the research and its potential impact.
Keywords
Breast cancer, machine learning, classification, diagnosis, treatment, risk factors, system design, data analysis, system testing, empirical studies.
Frequently Asked Questions: Machine Learning-Based Breast Cancer Classification System
What is the main objective of this study?
The primary objective is to develop a machine learning-based system for the accurate and efficient classification of breast cancer. This aims to improve the diagnostic process.
What are the key themes explored in this study?
Key themes include the application of machine learning to breast cancer classification, analysis of various machine learning techniques, development and evaluation of a breast cancer classification system, exploration of the advantages and disadvantages of using machine learning for breast cancer diagnosis, and a review of existing literature on breast cancer diagnosis and treatment.
What does Chapter One cover?
Chapter One provides an introduction to the study, including background information on breast cancer, the problem statement, aims and objectives, research questions, significance and scope of the study, limitations, and operational definitions of key terms.
What is discussed in Chapter Two?
Chapter Two presents a comprehensive literature review encompassing an overview of breast cancer (risk factors, symptoms, screening, diagnosis, treatment, types), a discussion of machine learning techniques in breast cancer diagnosis and treatment (including advantages and disadvantages), and a review of relevant empirical studies.
What is detailed in Chapter Three?
Chapter Three details the system design and analysis, including data collection methods, design languages, tools, and techniques, analysis of existing and proposed systems, and the functionalities of the developed system.
What does Chapter Four encompass?
Chapter Four focuses on the implementation and documentation of the breast cancer classification system, outlining system testing procedures (test plan, test data, and results) demonstrating the system's performance and effectiveness.
What is included in Chapter Five?
Chapter Five presents a summary, conclusions, recommendations, and suggestions for future studies based on the research findings.
What are the key words associated with this study?
Key words include: Breast cancer, machine learning, classification, diagnosis, treatment, risk factors, system design, data analysis, system testing, and empirical studies.
What type of system is being developed?
A machine learning-based system for the classification of breast cancer is being developed.
What is the expected outcome of this study?
The expected outcome is a functional machine learning system that improves the accuracy and efficiency of breast cancer diagnosis.
What kind of data is used in this study?
The provided text doesn't specify the exact type of data, but it mentions data collection methods used in the system development. The data likely relates to patient information relevant for breast cancer diagnosis.
- Quote paper
- Akor Ugwu (Author), 2020, Breast Cancer Classification Using Machine Learning. An Empirical Study, Munich, GRIN Verlag, https://www.grin.com/document/1012996
