In this paper, a novel two-stage R-CNN network called ParallelNet is proposed for thigh fracture detection task. In the proposed method, multiple parallel backbone networks and a feature fusion connection structure are designed, which can extract features with different reception fields. Specifically, the first backbone network is denoted as main network, which adopted normal convolution to detect small fractures, the rest backbone networks are denoted as sub-networks which adopted dilated convolution to detect large fractures. We evaluated the proposed method on a thigh fracture dataset containing 3842 X-ray radiographs, 3484 of which is assigned as a training dataset and 358 as a testing dataset. The experiments compare the proposed method with other state-of-the-art deep learning frameworks, including Faster R-CNN, FPN, Cascade R-CNN and RetinaNet, especially DCFPN which focus on thighbone fracture detection task. Our framework achieved 87.8% AP50 and 49.3% AP75 which outperformed other state-of-the-art frameworks. Moreover, ablation experiments on the backbone numbers, connection styles, different dilation rates and the position of dilated convolution have been attempted, and the function of each hyperparameter is analyzed.

在本文中，提出了一种新颖的两阶段R-CNN网络，称为ParallelNet，用于大腿骨折的检测。该方法设计了多个并行骨干网和特征融合连接结构，可以提取具有不同接收场的特征。具体来说，第一个骨干网被称为主网络，它采用正常卷积来检测小裂缝，其余的骨干网被称为子网络，它采用膨胀卷积来检测大裂缝。我们在包含3842张X射线照片的大腿骨折数据集上评估了该方法，其中3484张X射线照片被指定为训练数据集，而358个为测试数据集。实验将提出的方法与其他最新的深度学习框架进行了比较，包括Faster R-CNN，FPN，级联R-CNN和RetinaNet，尤其是DCFPN，它们专注于大腿骨骨折的检测任务。我们的框架实现了87.8％的AP50和49.3％的AP75，优于其他最新框架。此外，尝试了骨干数目，连接方式，不同的扩张速率和扩张卷积位置的消融实验，并分析了每个超参数的功能。