IDEAS home Printed from https://ideas.repec.org/a/ids/ijnvor/v27y2022i2p107-124.html
   My bibliography  Save this article

A deep learning model framework for diabetic retinopathy detection

Author

Listed:
  • M. Padmapriya
  • S. Pasupathy
  • R. Sumathi
  • V. Punitha

Abstract

Diabetic retinopathy (DR) is the typical diabetic eye issue and a main reason of blindness around the world. As per the International Diabetes Federation (IDF), the rates of diabetes would rise to 552 million by 2034. Breakthroughs in computer science techniques inclusive of artificial intelligence (AI) and deep learning (DL) have multiplied opportunities for early detection of DR. This indicates that the likelihood of a patient's healing will improve, and the risk of eyesight loss could be minimised in due course. A deep learning model (ResNet) for medical DR detection was examined in this article. The dataset of Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 was used to train and test the DL model. To demonstrate the vitality of the chosen ResNet model, performance measures and testing accuracy like recall, precision, and F1 score were determined. The modified ResNet model attained a testing accuracy of around 84% even with only a few dataset images. Also, training time and computational complexity were reduced with this simpler model.

Suggested Citation

  • M. Padmapriya & S. Pasupathy & R. Sumathi & V. Punitha, 2022. "A deep learning model framework for diabetic retinopathy detection," International Journal of Networking and Virtual Organisations, Inderscience Enterprises Ltd, vol. 27(2), pages 107-124.
    • Handle: RePEc:ids:ijnvor:v:27:y:2022:i:2:p:107-124
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=127584
    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:ijnvor:v:27:y:2022:i:2:p:107-124. 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=22 .

    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.