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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月27日

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  • Zhengshui Xu,
  • Tianle Shen,
  • Changchun Ye,
  • Yu Li,
  • Danwen Zhao,
  • Ming Zhang,
  • Yao Cheng,
  • Jintao Chai,
  • Jiantao Jiang,
  • Junfeng Xi,
  • Chao Xu,
  • Wei Chen,
  • Shiyuan Liu

摘要

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.