Machine Learning for the Identification of Bone Deformities

Mohammed Aslam Khan -, Mohd. Yaseen Ahmed -, Syed Safadar Hussain -, Khutaija Abid -
{"title":"Machine Learning for the Identification of Bone Deformities","authors":"Mohammed Aslam Khan -, Mohd. Yaseen Ahmed -, Syed Safadar Hussain -, Khutaija Abid -","doi":"10.37082/ijirmps.v11.i1.230311","DOIUrl":null,"url":null,"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.","PeriodicalId":246139,"journal":{"name":"International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences","volume":"50 ","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37082/ijirmps.v11.i1.230311","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

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.
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信