X-ray imaging is the primary diagnostic tool for clinical diagnosis of suspected fracture. Hand fracture (HF) is a world-leading health problem for children, adolescents and the elderly. A missed diagnosis of hand fracture on radiography may lead to severe consequences for patients, resulting in delayed treatment and poor recovery of function. Nevertheless, many hand fractures are fairly insidious, which often lead to misdiagnosis. In this dissertation, we propose GA_Faster R-CNN in which a guided anchoring method (GA) of GA_RPN is applied to detect and localize hand fractures in radiographs. Our new guided anchoring method makes the anchor generation more accurate and efficient, greatly improves the network performance, and saves computing power. In our work, Feature Pyramid Network (FPN) is used to solve the problem of tiny object detection which mostly appears at the joint of fingertips and knuckles. In addition, Balanced L1 Loss is applied to adapt to the imbalance of learning tasks. We evaluate the proposed algorithm on a HF dataset containing 3,067 X-ray radiographs, 2,453 of which are assigned as the training dataset and 614 as the testing dataset. The present framework achieved accuracies of 97%–99% and an average precision (AP) of 70.7%, thereby outperforming the previous state-of-the-art methods for detecting HF. As a consequence, the GA_Faster R-CNN has great potential for clinical applications.

X射线成像是可疑骨折临床诊断的主要诊断工具。对于儿童，青少年和老年人，手部骨折（HF）是世界领先的健康问题。放射线照相术未正确诊断出手部骨折可能会给患者带来严重后果，导致治疗延迟和功能恢复不良。然而，许多手部骨折相当隐蔽，经常导致误诊。本文提出了一种GA_Faster R-CNN，其中GA_RPN的导向锚定方法（GA）用于在射线照相中检测和定位手部骨折。我们的新型引导锚定方法使锚定生成更加准确和高效，大大提高了网络性能，并节省了计算能力。在我们的工作中 特征金字塔网络（FPN）用于解决微小对象检测问题，该问题通常出现在指尖和指关节的关节处。另外，平衡的L1损失适用于适应学习任务的不平衡。我们在包含3,067张X射线照片的HF数据集上评估提出的算法，其中2,453张X射线照片被指定为训练数据集，而614被指定为测试数据集。本框架实现了97％–99％的准确度和70.7％的平均精确度（AP），从而胜过了先前用于检测HF的最新技术。因此，GA_Faster R-CNN具有很大的临床应用潜力。我们在包含3,067张X射线照片的HF数据集上评估提出的算法，其中2,453张X射线照片被指定为训练数据集，而614被指定为测试数据集。本框架实现了97％–99％的准确度和70.7％的平均精确度（AP），从而胜过了先前用于检测HF的最新技术。因此，GA_Faster R-CNN具有很大的临床应用潜力。我们在包含3,067张X射线照片的HF数据集上评估提出的算法，其中2,453张X射线照片被指定为训练数据集，而614被指定为测试数据集。本框架实现了97％–99％的准确度和70.7％的平均精确度（AP），从而胜过了先前用于检测HF的最新技术。因此，GA_Faster R-CNN具有很大的临床应用潜力。