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卷积神经网络在类风湿性关节炎X光影像自动识别的应用及效果分析

Study on Application and Effect of Convolutional Neural Network in Automatic Recognition of Rheumatoid Arthritis X-ray Images
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摘要 X光影像对评估关节骨密度和骨侵蚀程度具有重要意义。然而,以往临床多采用人工直接评估类风湿性关节炎X光影像的方法,一直缺乏X光影像识别方面的计算机量化诊断数据。本研究通过使用深度学习中卷积神经网络方法自动通过X光影像判断患者患有类风湿性关节炎的严重程度,该方法可以极大提高诊断的自动化水平,降低医疗工作者工作量,可以得到更为客观的诊断结果。 Rheumatoid arthritis is an inflammatory immune disease characterized by progressive destruction of joints.In recent years,X-ray imaging has been widely used in clinical diagnosis.It is of great significance for evaluating joint bone density and bone erosion.However,in the past,artificial direct evaluation of rheumatoid arthritis X-ray images has been used in clinical practice,and there has been a lack of computerized diagnostic data for X-ray image recognition.This study used the convolutional neural network method in deep learning to automatically determine the severity of rheumatoid arthritis from X-ray images.This method greatly improved the level of automation of diagnosis,reduced the workload of medical workers,and provided more objective diagnosis results.
作者 魏巍 徐卫峰 WEI Wei;XU Wei-feng(Zhuji People’s Hospital of Zhejiang Province)
机构地区 浙江省诸暨市人民医院
出处 《医院管理论坛》 2020年第7期72-74,共3页 Hospital Management Forum
关键词 类风湿性关节炎 X光影像 深度学习 卷积神经网络 Rheumatoid arthritis X-ray images Deep learning Convolutional neural network
分类号 R197.3 [医药卫生—卫生事业管理]
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