摘要
骨关节炎是中老年人群最常见的关节疾病,该疾病及其并发症占据了全球10%的医疗问题。其中膝关节骨关节炎最为严重,致残风险极高。尽早发现并介入治疗对于缓解其症状,减少其危害有着至关重要的意义。首先收集了大量膝关节DR影像数据,对获得的数据进行多种纹理特征和融合特征的提取,将提取的特征向量进行各种组合作为输入训练SVM模型,使用网格搜索法进行了进行参数寻优。训练完成的模型在测试集上的准确率最高可以达到84.29%,具有良好的智能分类诊断性能。使用训练完的SVM模型,可以有效的对膝关节骨性关节炎进行分级,辅助医生进行诊断,对膝关节骨关节炎的早期诊断,尽早介入治疗有着重要意义。
Osteoarthritis is the most common joint disease in middle-aged and elderly people. The disease and its complications account for 10 per cent of global medical problems. Among them, osteoarthritis of the knee is the most serious and the risk of disability is very high. Early detection and interventional treatment is of great significance to relieve the symptoms and reduce the harm. Firstly, a large number of knee joint DR image data were collected. Various texture features and fusion features were extracted from the obtained data. Then, various combinations of extracted feature vectors were used as input to the training support vector machine model. We use the grid search method to optimize the parameters. The highest accuracy of the trained model on the test set can reach 84.29%, which has good intelligent classification and diagnosis performance. Using the trained SVM model can effectively grade knee osteoarthritis and assist doctors in diagnosis, which is of great significance for early diagnosis and early intervention treatment of knee osteoarthritis.
作者
刘志鹏
李修寒
冯锐
姚庆强
王伟
吴小玲
Liu Zhipeng;Li Xiuhan;Feng Rui;Yao Qingqiang;Wang Wei;Wu Xiaoling(Department of Biomedical Engineering,Nanjing Medical University,Nanjing 210000,China;Nanjing First Hospital,Nanjing 210000,China)
出处
《电子测量技术》
北大核心
2021年第5期129-134,共6页
Electronic Measurement Technology
基金
国家重点研发计划(2017YFB1303203)
江苏省研究生研究与实践创新计划(JX12413673)资助。
关键词
SVM
纹理特征
融合特征
特征选择
SVM
texture feature
fusion feature
feature selection
