改进YOLOv8检测肺结节，提升小结节识别精度与泛化能力。
CPLOYO: A Pulmonary Nodule Detection Model with Multi-Scale Feature Fusion and Nonlinear Feature Learning
- 融合多尺度特征与非线性学习，增强小结节检测能力。
- 在LUNA16数据集上优于YOLOv9和RT-DETR等主流模型。
- 适合医学影像分析、肺癌早期筛查场景使用。
物联网技术在肺结节检测中的应用显著提升了系统的智能化与实时性。当前肺结节检测主要针对实性结节，但不同类型结节对应不同肺癌形式，多类型检测有助于提高整体肺癌检出率并提升治愈率。为实现高灵敏度检测，对YOLOv8模型进行针对性改进：首先引入C2f_RepViTCAMF模块替代主干网络中的C2f模块，提升小结节检测精度并实现轻量化设计；其次加入MSCAF模块重构特征融合部分，改善不同尺度结节的检测性能；此外，集成KAN网络以利用其强大的非线性特征学习能力，进一步提升小结节检测精度与模型泛化能力。在LUNA16数据集上的测试表明，改进模型在各项评估指标上均优于原模型及YOLOv9、RT-DETR等主流模型。
原文摘要 · Abstract (English)
The integration of Internet of Things (IoT) technology in pulmonary nodule detection significantly enhances the intelligence and real-time capabilities of the detection system. Currently, lung nodule detection primarily focuses on the identification of solid nodules, but different types of lung nodules correspond to various forms of lung cancer. Multi-type detection contributes to improving the overall lung cancer detection rate and enhancing the cure rate. To achieve high sensitivity in nodule detection, targeted improvements were made to the YOLOv8 model. Firstly, the C2f\_RepViTCAMF module was introduced to augment the C2f module in the backbone, thereby enhancing detection accuracy for small lung nodules and achieving a lightweight model design. Secondly, the MSCAF module was incorporated to reconstruct the feature fusion section of the model, improving detection accuracy for lung nodules of varying scales. Furthermore, the KAN network was integrated into the model. By leveraging the KAN network's powerful nonlinear feature learning capability, detection accuracy for small lung nodules was further improved, and the model's generalization ability was enhanced. Tests conducted on the LUNA16 dataset demonstrate that the improved model outperforms the original model as well as other mainstream models such as YOLOv9 and RT-DETR across various evaluation metrics.
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