IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-030-41862-5_46.html
   My bibliography  Save this book chapter

Qualitative Study on Data Mining Algorithms for Classification of Mammogram Images

In: New Trends in Computational Vision and Bio-inspired Computing

Author

Listed:
  • N. Arivazhagan

    (SRM University, School of Computing)

  • S. Govindarajan

    (SRM University, School of Public Health)

Abstract

Automatic detection of cancer by digital images using data mining algorithm is required for next generation medical data analysis system. Classification of mammogram images for cancer detection is carried out with different machine learning algorithms as SVM (Support vector machine), KNN, Naive Bayesian. Analysing these algorithms are carried out to identify a technique that can provide the best possible result for all variant of images. This paper presents a qualitative study on these algorithms. The result of the analysis declares that SVM with bagging provides the better performance in the classification of mammogram images with accuracy of 99.467%.

Suggested Citation

  • N. Arivazhagan & S. Govindarajan, 2020. "Qualitative Study on Data Mining Algorithms for Classification of Mammogram Images," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 483-489, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_46
    DOI: 10.1007/978-3-030-41862-5_46
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    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:spr:sprchp:978-3-030-41862-5_46. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.