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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/113000


    Title: Development of a Deep Learning Model for Chest X-Ray Screening
    Authors: Hsu, W.H.;Tsai, F.J.;Zhang, G.;Chang, C.K.;Hsieh, P.H.;Yang, S.N.;Sun, S.S.;Liao, KenYK;黃宗祺;HUANG, TZUNG-CHI
    Contributors: 生物資訊與醫學工程學系
    Keywords: Deep learning;Chest X-Ray
    Date: 2019-12
    Issue Date: 2020-09-07 14:05:35 (UTC+8)
    Publisher: 亞洲大學
    Abstract: Developed in recent years, deep neural network
    becomes the best method for rapid analysis of advanced
    features and automation in medical image analysis. As a
    second clinical opinion provided by artificial intelligence (AI),
    it can reduce the physician's workload and reduce
    misjudgment. This study collected 365,892 chest X-ray images
    and clinical diagnosis reports through retrospective analysis,
    and compared five different input image sizes and images that
    generated by clinical labeling pre-processing in the
    classification model building and testing. An AI trained chest
    X-ray abnormal interpretation model by using DesNet121
    neural network gave a test accuracy of 0.875. Deep neural
    network shows the potential of accountable methods to help
    lung classifications for normal and abnormal screening in
    clinics.
    Relation: MEDICAL PHYSICS INTERNATIONAL
    Appears in Collections:[生物資訊與醫學工程學系 ] 期刊論文

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