IDEAS home Printed from https://ideas.repec.org/a/ids/ijenma/v10y2019i1p64-77.html
   My bibliography  Save this article

Automatic detection and classification of brain tumours using k-means clustering with classifiers

Author

Listed:
  • Narayanan Hema Rajini
  • Rajaram Bhavani

Abstract

A brain tumour detection and classification system has been designed and developed. This work presents a new approach to the automated detection and classification of astrocytoma, medulloblastoma, glioma, glioblastoma multiforme and craniopharyngioma type of brain tumours based on k-means clustering and texture features, which separate brain tumour from healthy tissues in magnetic resonance images. The magnetic resonance feature image used for the tumour detection consists of T2-weighted magnetic resonance images for each axial slice through the head. The application of the proposed method for tracking tumour is demonstrated to help pathologists distinguish exactly tumour region and its type of tumour. The results are quantitatively evaluated by a human expert. The average overlap metric, average precision and the average recall between the results obtained using the proposed approach and ground truth are 0.92, 0.97 and 0.92, respectively. A classification with accuracy of 100%, 99% and 98% has been obtained by SVM, ANN and decision tree.

Suggested Citation

  • Narayanan Hema Rajini & Rajaram Bhavani, 2019. "Automatic detection and classification of brain tumours using k-means clustering with classifiers," International Journal of Enterprise Network Management, Inderscience Enterprises Ltd, vol. 10(1), pages 64-77.
    • Handle: RePEc:ids:ijenma:v:10:y:2019:i:1:p:64-77
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=98103
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:ids:ijenma:v:10:y:2019:i:1:p:64-77. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=187 .

    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.