IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-030-41862-5_102.html
   My bibliography  Save this book chapter

Classification of Multi-retinal Disease Based on Retinal Fundus Image Using Convolutional Neural Network

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

Author

Listed:
  • A. Vanita Sharon

    (SRM Institute of Science and Technology, Big Data Analytics)

  • G. Saranya

    (SRM Institute of Science and Technology)

Abstract

Retinal disease often refers to retinal vascular disease is currently growing tremendously in the field of ophthalmology which is to be diagnosed early to prevent from blindness. In recent days, many retinal diseases that cause damage to retina due to abnormal blood flow. In this paper, multiclass classification is been proposed for four classes of diseases such as Arteriosclerotic Retinopathy, Background Diabetic Retinopathy, Choroidal Neovascularization, Hypertensive Retinopathy. The proposed model uses convolutional neural network which extracts its own features and classify them when compared to other classification methods. The system uses ALEXNET architecture which is deeper with more filters that can extract the features of the image automatically and classifies them to predict the class of disease it belongs to. The model is trained over the data which is been collected from STRARE database. As a result, the model is able to achieve the prediction of test case and classifies the disease which brings betterment in diagnosis of Retinal Diseases and avoids from blindness.

Suggested Citation

  • A. Vanita Sharon & G. Saranya, 2020. "Classification of Multi-retinal Disease Based on Retinal Fundus Image Using Convolutional Neural Network," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 1009-1016, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_102
    DOI: 10.1007/978-3-030-41862-5_102
    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_102. 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.