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    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

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