Radiologists interprets X-ray samples by visually inspecting them to diagnose the presence of fractures in various bones. Interpretation of radiographs is a time-consuming and intense process involving manual examination of fractures. In addition, clinician’s shortage in medically under-resourced areas, unavailability of expert radiologists in busy clinical settings or fatigue caused due to demanding workloads could lead to false detection rate and poor recovery of the fractures. A comprehensive study is imparted here covering fracture diagnosis with the aim to assist investigators in developing models that automatically detects fracture in human bones. The paper is presented in five folds. Firstly, we discuss data preparation stage. Second, we present various image-processing techniques used for fracture detection. Third, we analyze conventional and deep learning based techniques for diagnosing bone fractures. Fourth, we make comparative analysis of existing techniques. Fifth, we discuss different issues and challenges faced by researches while dealing with fracture detection.

中文翻译：

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骨X线影像骨折检测技术综述

放射科医生通过目视检查 X 射线样本来解释 X 射线样本，以诊断各种骨骼中是否存在骨折。X 光片的解释是一个耗时且紧张的过程，涉及手动检查骨折。此外，在医疗资源匮乏地区临床医生短缺、在繁忙的临床环境中没有专家放射科医生或因工作量繁重而导致疲劳可能导致骨折的误检率和恢复不良。这里进行了一项涵盖骨折诊断的综合研究，旨在帮助研究人员开发自动检测人体骨骼骨折的模型。这篇论文以五折形式呈现。首先，我们讨论数据准备阶段。其次，我们介绍了用于裂缝检测的各种图像处理技术。第三，我们分析了用于诊断骨折的传统和基于深度学习的技术。第四，我们对现有技术进行比较分析。第五，我们讨论了在处理裂缝检测时研究面临的不同问题和挑战。