Machine Learning for the Identification of Bone Deformities

Mohammed Aslam Khan -, Mohd. Yaseen Ahmed -, Syed Safadar Hussain -, Khutaija Abid -
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Abstract

The success of machine learning algorithms in medical imaging has boosted the demand for models that have been artificially trained to function more rapidly and effectively in the medical profession. In this paper, a method for identifying bone fractures using machine learning algorithms is presented, which can help to lighten the workload of orthopedics. Instead of spending hours in radiology departments, the substantial application of machine learning in this era of huge medical data will make it possible to obtain information from the available X-ray images. The imaging techniques described in this study can quickly determine whether a bone fracture has occurred in a human body after an X-ray has been obtained.
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骨畸形识别的机器学习
机器学习算法在医学成像领域的成功推动了对人工训练模型的需求,这些模型可以在医疗行业中更快速、更有效地发挥作用。本文提出了一种利用机器学习算法识别骨折的方法,这有助于减轻骨科的工作量。在这个庞大的医疗数据时代,机器学习的大量应用将使从可用的x射线图像中获取信息成为可能,而不是在放射科花费数小时。本研究中描述的成像技术可以在获得x射线后快速确定人体是否发生骨折。
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