IDEAS home Printed from https://ideas.repec.org/a/igg/jsir00/v14y2023i1p1-12.html
   My bibliography  Save this article

CT Image Detection of Pulmonary Tuberculosis Based on the Improved Strategy YOLOv5

Author

Listed:
  • Jing Liu

    (Beijing Chest Hospital, Capital Medical University, China & The Fifth People's Hospital of Suzhou, China)

  • Haojie Xie

    (Changshu Institute of Technology, China)

  • Mingli Lu

    (Changshu Institute of Technology, China)

  • Ye Li

    (Beijing Chest Hospital, Capital Medical University, China)

  • Bing Wang

    (Beijing Chest Hospital, Capital Medical University, China)

  • Zhaogang Sun

    (Beijing Tuberculosis and Thoracic Tumor Research Institute, China)

  • Wei He

    (Beijing Chest Hospital, Capital Medical University, China)

  • Limin Wen

    (Infectious Disease Hospital of Heilongjiang Province, China)

  • Dailun Hou

    (Beijing Chest Hospital, Capital Medical University, China)

Abstract

The diagnosis of pulmonary tuberculosis is a complicated process with a long wait. According to the WS 288-2017 standard, PTB can be divided into five types of imaging. To date, no relevant studies on PTB CT images based on the Yolov5 algorithm have been retrieved. To develop an improved strategy YOLOv5, for the classification of PTB lesions based on whole, CT slices were combined with three other modules. CT slices of PTB collected from hospitals were set as training, verification, and external test sets. It is compared with YOLOv5, SSD and RetinaNet neural network methods. The values of precision, recall, MAP, and F1-score of the improved strategy YOLOv5 for the external test were 0.707, 0.716, 0.715, and 0.71. In this study, based on the same dataset, the improved strategy YOLOv5 model has better results than other networks. Our method provides an effective method for the timely detection of PTB.

Suggested Citation

  • Jing Liu & Haojie Xie & Mingli Lu & Ye Li & Bing Wang & Zhaogang Sun & Wei He & Limin Wen & Dailun Hou, 2023. "CT Image Detection of Pulmonary Tuberculosis Based on the Improved Strategy YOLOv5," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 14(1), pages 1-12, January.
    • Handle: RePEc:igg:jsir00:v:14:y:2023:i:1:p:1-12
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSIR.329217
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jsir00:v:14:y:2023:i:1:p:1-12. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.