English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 60916/93528 (65%)
造訪人次 : 1558943      線上人數 : 23
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/121448

    題名: Modified YOLOv4-DenseNet Algorithm for Detection of Ventricular Septal Defects in Ultrasound Images
    作者: Chen, S. H.;Wang, C. W.;Tai, I. H.;Weng, Ken-Pen;Chen, Y. H.;Hsieh, K. S.
    關鍵詞: (VSD);Doppler;Echocardiographic;Images;Object;Detection;Deep;Learning;YOLOv4
    日期: 2021-06-08
    上傳時間: 2021-10-06 12:11:36 (UTC+8)
    摘要: Doctors conventionally analyzed echocardiographic images for diagnosing congenital heart diseases (CHDs).
    However, this process is laborious and depends on the experience of the doctors. This study investigated the use of deep learning algorithms for the image detection of the ventricular septal defect (VSD), the most common type. Color Doppler echocardiographic images containing three types of VSDs were tested with color doppler ultrasound medical images. To the best of our knowledge, this study is the first one to solve this object detection problem by using a modified YOLOv4–DenseNet framework. Because some techniques of YOLOv4 are not suitable for echocardiographic object detection, we revised the algorithm for this problem. The results revealed that the YOLOv4–DenseNet outperformed YOLOv4, YOLOv3, YOLOv3–SPP, and YOLOv3–DenseNet in terms of metric mAP-50. The F1-score of YOLOv4-DenseNet and YOLOv3-DenseNet were better than those of others. Hence, the contribution of this study establishes the feasibility of using deep learning for echocardiographic image detection of VSD investigation and a better YOLOv4-DenseNet framework could be employed for the VSD detection.
    關聯: International Journal of Interactive Multimedia and Artificial Intelligence 6(7), p.101-108
    DOI: 10.9781/ijimai.2021.06.001
    顯示於類別:[資訊工程學系暨研究所] 期刊論文


    檔案 描述 大小格式瀏覽次數



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