Chapter ONE

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 Research
1.9 Definition of Terms

Chapter TWO

2.1 Overview of Biomedical Image Processing
2.2 Gaussian Filter Techniques in Image Processing
2.3 Applications of Gaussian Filters in Biomedical Image Processing
2.4 Challenges in Biomedical Image Processing
2.5 State-of-the-Art Techniques in Lung Cancer Detection
2.6 Previous Studies on Gaussian Filters for Lung Cancer Detection
2.7 Advancements in Image Processing Technology
2.8 Impact of Image Quality on Lung Cancer Detection
2.9 Role of Machine Learning in Biomedical Image Processing
2.10 Future Trends in Biomedical Image Processing

Chapter THREE

3.1 Research Methodology Overview
3.2 Selection of Image Dataset for Study
3.3 Preprocessing Techniques for Biomedical Images
3.4 Implementation of Gaussian Filter Algorithm
3.5 Integration of Machine Learning Models
3.6 Evaluation Metrics for Lung Cancer Detection
3.7 Statistical Analysis Methods
3.8 Data Collection and Analysis Procedures

Chapter FOUR

4.1 Analysis of Experimental Results
4.2 Comparative Study of Gaussian Filter Variants
4.3 Performance Evaluation of Lung Cancer Detection Models
4.4 Impact of Image Quality Enhancement Techniques
4.5 Correlation Analysis between Image Features and Cancer Detection
4.6 Discussion on Algorithm Efficiency and Accuracy
4.7 Interpretation of Statistical Findings
4.8 Recommendations for Future Research

Chapter FIVE

5.1 Conclusion and Summary
5.2 Key Findings Recap
5.3 Contributions to Biomedical Image Processing Field
5.4 Implications of Research Results
5.5 Limitations and Future Research Directions