标题： 轻量级G-YOLOv11：推进儿童腕部X射线骨折检测的效率 显示英文标题

标题： Lightweight G-YOLOv11: Advancing Efficient Fracture Detection in Pediatric Wrist X-rays

摘要： 计算机辅助诊断（CAD）系统极大地提高了放射科医生和外科医生对医学影像的解读能力。然而，目前用于检测X射线图像中骨折的CAD系统主要依赖于大型且资源密集型的检测器，这限制了它们在临床环境中的实用性。为了解决这一局限性，我们提出了一种基于YOLO检测器的新轻量级CAD系统，用于骨折检测。该系统名为基于鬼卷积的YOLOv11（G-YOLOv11），建立在YOLO检测器家族的最新版本之上，并结合了鬼卷积操作以提取特征。鬼卷积操作生成与传统卷积相同数量的特征图，但所需的线性运算更少，从而降低了检测器的计算资源需求。我们在GRAZPEDWRI-DX数据集上评估了所提出的G-YOLOv11检测器的性能，在NVIDIA A10 GPU上的推理时间为2.4毫秒，mAP@0.5达到0.535。与标准YOLOv11相比，G-YOLOv11在mAP@0.5上减少了13.6%，在模型大小上减少了68.7%。这些结果在效率方面建立了新的最先进基准，超过了现有的检测器。代码和模型可在https://github.com/AbdesselamFerdi/G-YOLOv11获取。 显示英文摘要

摘要： Computer-aided diagnosis (CAD) systems have greatly improved the interpretation of medical images by radiologists and surgeons. However, current CAD systems for fracture detection in X-ray images primarily rely on large, resource-intensive detectors, which limits their practicality in clinical settings. To address this limitation, we propose a novel lightweight CAD system based on the YOLO detector for fracture detection. This system, named ghost convolution-based YOLOv11 (G-YOLOv11), builds on the latest version of the YOLO detector family and incorporates the ghost convolution operation for feature extraction. The ghost convolution operation generates the same number of feature maps as traditional convolution but requires fewer linear operations, thereby reducing the detector's computational resource requirements. We evaluated the performance of the proposed G-YOLOv11 detector on the GRAZPEDWRI-DX dataset, achieving an mAP@0.5 of 0.535 with an inference time of 2.4 ms on an NVIDIA A10 GPU. Compared to the standard YOLOv11l, G-YOLOv11l achieved reductions of 13.6% in mAP@0.5 and 68.7% in size. These results establish a new state-of-the-art benchmark in terms of efficiency, outperforming existing detectors. Code and models are available at https://github.com/AbdesselamFerdi/G-YOLOv11.