English  |  正體中文  |  简体中文  |  Items with full text/Total items : 92324/107581 (86%)
Visitors : 18325445      Online Users : 223
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version

    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
    Appears in Collections:[生物資訊與醫學工程學系 ] 期刊論文

    Files in This Item:

    File Description SizeFormat

    All items in ASIAIR are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback