English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 57615/91160 (63%)
造访人次 : 13558125      在线人数 : 263
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻

    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/118220

    题名: Prediction of Chronic Kidney Disease Stages by Renal Ultrasound Imaging
    作者: Chen, Chi-Jim;Pai, Tun-Wen;Hsu, Hui-Huang;Lee, Chien-Hung;Chen, Kuo-Su;Chen, Yung-Chih
    关键词: Ultrasonography;support vector machine;feature extraction;chronic kidney disease;estimated glomerular filtration rate(eGFR)
    日期: 2020-01
    上传时间: 2020-03-09 12:10:13 (UTC+8)
    摘要: To detect chronic kidney disease (CKD) at earlier stages, diagnosis through non-invasive ultrasonographic imaging techniques provides an auxiliary clinical approach for at-risk CKD patients. We have established a detection method based on imaging processing techniques and machine learning approaches for the diagnosis of different CKD stages. Decisive area-proportional and textural features and support-vector-machine techniques were applied for efficient and effective analyses. Several clustered collections of CKD patients were evaluated and compared according to the estimated glomerular filtration rates. Based on the findings of evolving changes from ultrasound images, the proposed approach could be used as complementary evidences to help differentiate between different clinical diagnoses.
    關聯: Enterprise Information Systems 14(2), p.178-195
    DOI: 10.1080/17517575.2019.1597386
    显示于类别:[資訊工程學系暨研究所] 期刊論文


    档案 描述 大小格式浏览次数



    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回馈