Developing a CNN-Based Tumor Segmentation Model Utilizing the Segment Anything Model for Brain Imaging

1. Introduction 1.1 Background 1.2 Research Objectives 1.3 Scope of the Study 2. Literature Review 2.1 Tumor Segmentation Techniques 2.2 Convolutional Neural Networks (CNN) 2.3 Segment Anything Model Fundamentals 3. Theoretical Framework 3.1 CNN Architecture 3.2 Image Segmentation Concepts 3.3 Brain Imaging Modalities 4. Methodology 4.1 Data Collection and Preprocessing 4.2 Model Training Procedures 4.3 Evaluation Metrics 5. Model Development 5.1 Design of the CNN Model 5.2 Integration with Segment Anything 5.3 Computational Requirements 6. Experimental Results 6.1 Accuracy and Precision Analysis 6.2 Comparison with Existing Models 6.3 Limitations Observed 7. Discussion 7.1 Interpretation of Results 7.2 Implications for Clinical Practices 7.3 Future Research Directions 8. Conclusion 8.1 Summary of Findings 8.2 Contributions to the Field 8.3 Final Remarks