IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v11y2025i6id1775.html

Review of Lung Cancer Detection and Classification Image Processing Techniques

Author

Listed:
  • Noreen Sarai
  • Wendy Gapare
  • Taurai George Rebanowako

Abstract

The aim of this review is to present an overview of Lung Cancer Detection and Classification Using Digital Image Processing computer aided design systems. The discovery of cellular breakdown in the lungs through picture handling was a significant apparatus for the analysis. Several techniques were presented with the goal of having a thorough understanding of the techniques that are used during lung cancer detection. The techniques analysed in this review includes, CT scan examination, thresholding technique, nodule segmentation techniques, pre-processing and segmentation techniques and classification techniques. Models used during classification were also analysed with the goal of presenting a comprehensive analysis of these techniques as they are used during lung cancer detection. CAD was found to be the most popular technique being used.

Suggested Citation

  • Noreen Sarai & Wendy Gapare & Taurai George Rebanowako, 2025. "Review of Lung Cancer Detection and Classification Image Processing Techniques," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(6), pages 155-165, December.
  • Handle: RePEc:jbh:ijsrcs:v11:y2025:i6:id:1775
    DOI: 10.32628/CSEIT2511624
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2511624
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/home/article/view/CSEIT2511624
    File Function: Article URL
    Download Restriction: no

    File URL: https://ijsrcseit.com/home/article/download/CSEIT2511624/CSEIT2511624
    File Function: Full text
    Download Restriction: no

    File URL: https://libkey.io/10.32628/CSEIT2511624?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:jbh:ijsrcs:v11:y2025:i6:id:1775. 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: Pankaj Sharma (USA) (email available below). General contact details of provider: https://ijsrcseit.com/home .

    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.