淡江大學機構典藏:Item 987654321/118220
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    Title: Prediction of Chronic Kidney Disease Stages by Renal Ultrasound Imaging
    Authors: Chen, Chi-Jim;Pai, Tun-Wen;Hsu, Hui-Huang;Lee, Chien-Hung;Chen, Kuo-Su;Chen, Yung-Chih
    Keywords: Ultrasonography;support vector machine;feature extraction;chronic kidney disease;estimated glomerular filtration rate(eGFR)
    Date: 2020-01
    Issue Date: 2020-03-09 12:10:13 (UTC+8)
    Abstract: 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.
    Relation: Enterprise Information Systems 14(2), p.178-195
    DOI: 10.1080/17517575.2019.1597386
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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