Precision Detection of Breast Cancer Lesions Using YOLOv12 and Feature Fusion Techniques

1. Introduction 1.1 Background and Motivation 1.2 Objectives of the Study 1.3 Structure of the Thesis 2. Literature Review 2.1 Overview of Breast Cancer Detection 2.2 Deep Learning in Medical Imaging 2.3 YOLO Algorithm Evolution 3. Methodology 3.1 Data Collection and Preparation 3.2 YOLOv12 Architecture Overview 3.3 Feature Fusion Techniques Used 4. Implementation 4.1 System Requirements and Tools 4.2 Model Training Process 4.3 Challenges and Solutions 5. Evaluation 5.1 Metrics for Performance Assessment 5.2 Comparative Analysis with Previous Models 5.3 Limitations of the Approach 6. Results and Discussion 6.1 Detection Accuracy Results 6.2 Impacts of Feature Fusion on Results 6.3 Interpretation of Key Findings 7. Case Studies 7.1 Successful Detection Scenarios 7.2 Missed Lesion Instances 7.3 Practical Implications 8. Conclusion and Future Work 8.1 Summary of Findings 8.2 Contributions to the Field 8.3 Recommendations for Future Research