Hierarchical Lung Cancer Detection Using CR-YOLO with Multi-Scale and Hypergraph Analysis

Overview

CR-YOLO, a novel deep learning framework, significantly improves lung cancer detection accuracy on CT scans by integrating multi-scale convolution, global-local attention, and hypergraph inference. It achieves a mean Average Precision of 92.5%, outperforming the YOLOv8n baseline by 4.1%, while enhancing interpretability through Grad-CAM visualization.

Background

Lung cancer remains a leading cause of cancer mortality worldwide, with early diagnosis critical to improving survival rates. Traditional image processing and machine learning methods have aided detection but face challenges with image complexity and noise. Deep learning, particularly convolutional neural networks, has advanced medical imaging analysis, yet detecting small pulmonary nodules and reasoning over complex features remains difficult. Single-stage detection models like YOLO offer efficient detection but require enhancements to address scale variation and relational reasoning in lung cancer diagnosis.

Data Highlights

Key Findings

• CR-YOLO incorporates a Cognitive Reasoning C2f (CR-C2f) module mimicking radiologist hierarchical workflow.
• Multi-scale Convolution (MSC) module enhances feature perception across varying nodule sizes.
• Global-Local Attention (GLA) Bottlenecks integrate local morphology with contextual dependencies effectively.
• Hypergraph Convolution (HGC) Refiner enables high-order relational inference improving diagnostic reasoning.
• CR-YOLO achieves a mean Average Precision of 92.5%, a 4.1% absolute improvement over YOLOv8n.
• Grad-CAM analysis demonstrates improved interpretability, highlighting relevant image regions for diagnosis.

Clinical Implications

The CR-YOLO framework offers a more accurate and interpretable tool for early lung cancer detection, potentially facilitating timely diagnosis and treatment. Its hierarchical and multi-scale approach addresses challenges of nodule size variability and complex image contexts, supporting clinicians with reliable automated analysis. Integration of such advanced models could enhance screening programs and reduce mortality through earlier intervention.

Conclusion

CR-YOLO represents a significant advancement in lung cancer detection from CT scans by combining multi-scale feature extraction and hypergraph-based reasoning. Its improved accuracy and interpretability highlight its promise as a clinical decision support tool for early diagnosis.

References

1. World Health Organization -- Global Cancer Statistics
2. Alkasas et al. -- Texture Analysis Using Enhanced Local Ternary Pattern
3. Aouadi et al. -- Radiomics-Based Random Forest Classifier for Lung Cancer Staging
4. Kumar Sahoo et al. -- Transfer Learning with YOLOv2 for Tumor Detection
5. Huang et al. -- 3D OSAF-YOLOv3 Model for Pulmonary Nodule Detection