Hierarchical reasoning for lung cancer detection: from multi-scale perception to hypergraph inference with CR-YOLO

Accurate detection of lung cancer from Computed Tomography (CT) scans is vital for improving patient survival but remains challenging for deep learning models, which struggle with scale variations of pulmonary nodules and the complex reasoning required for diagnosis. We propose CR-YOLO, a novel framework incorporating a Cognitive Reasoning C2f (CR-C2f) module that emulates a radiologist's hierarchical workflow. CR-YOLO employs a Multi-scale Convolution (MSC) module for robust feature perception, Global-Local Attention (GLA) Bottlenecks to integrate local morphology with contextual dependencies, and a Hypergraph Convolution (HGC) Refiner for high-order relational inference. Experiments demonstrate that CR-YOLO achieves a mean Average Precision (mAP) of 92.5%, a 4.1% absolute improvement over the YOLOv8n baseline. In addition to improved accuracy, CR-YOLO enhances interpretability through Grad-CAM analysis, highlighting its potential as a reliable and transparent tool for early lung cancer diagnosis.