IDEAS home Printed from https://ideas.repec.org/a/epw/ejeng0/v4y2019i2id61007.html

Classification of Red Blood Cells using Principal Component Analysis Technique

Author

Listed:
  • Jameela Ali Alkrimi

    (University of Babylon College of Dentistry, Babylon, Iraq)

  • Sherna Aziz Tome

    (University of Baghdad College of Medicine. Baghdad, Iraq)

  • Loay E. George

    (University of Baghdad, College of Science,Iraq)

Abstract

Principal component analysis (PCA) is based feature reduction that reduces the correlation of features. In this research, a novel approach is proposed by applying the PCA technique on various morphologies of red blood cells (RBCs). According to hematologists, this method successfully classified 40 different types of abnormal RBCs. The classification of RBCs into various distinct subtypes using three machine learning algorithms is important in clinical and laboratory tests for detecting blood diseases. The most common abnormal RBCs are considered as anemic. The RBC features are sufficient to identify the type of anemia and the disease that caused it. Therefore, we found that several features extracted from RBCs in the blood smear images are not significant for classification when observed independently but are significant when combined with other features. The number of feature vectors is reduced from 271 to 8 as time resuming in training and accuracy percentage increased to 98%.

Suggested Citation

  • Jameela Ali Alkrimi & Sherna Aziz Tome & Loay E. George, 2019. "Classification of Red Blood Cells using Principal Component Analysis Technique," European Journal of Engineering and Technology Research, European Open Science, vol. 4(2), pages 17-22, February.
    • Handle: RePEc:epw:ejeng0:v:4:y:2019:i:2:id:61007
    DOI: 10.24018/ejeng.2019.4.2.1007
    as

    Download full text from publisher

    File URL: https://eu-opensci.org/index.php/ejeng/article/view/61007
    File Function: Abstract page
    Download Restriction: no
    File URL: https://eu-opensci.org/index.php/ejeng/article/download/61007/12042
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.24018/ejeng.2019.4.2.1007?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:epw:ejeng0:v:4:y:2019:i:2:id:61007. 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: Support (email available below). General contact details of provider: https://eu-opensci.org/index.php/ejeng .

    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.