IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v6y2021i2p14-d492119.html
   My bibliography  Save this article

Retinal Fundus Multi-Disease Image Dataset (RFMiD): A Dataset for Multi-Disease Detection Research

Author

Listed:
  • Samiksha Pachade

    (Center of Excellence in Signal and Image Processing, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded 431606, India)

  • Prasanna Porwal

    (Center of Excellence in Signal and Image Processing, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded 431606, India)

  • Dhanshree Thulkar

    (Electronics Department, Veermata Jijabai Technological Institute, Mumbai 400019, India)

  • Manesh Kokare

    (Center of Excellence in Signal and Image Processing, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded 431606, India)

  • Girish Deshmukh

    (Eye Clinic, Sushrusha Hospital, Nanded 431601, India)

  • Vivek Sahasrabuddhe

    (Department of Ophthalmology, Shankarrao Chavan Government Medical College, Nanded 431606, India)

  • Luca Giancardo

    (Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston (UTHealth), Houston, TX 77030, USA)

  • Gwenolé Quellec

    (Inserm, UMR 1101, F-29200 Brest, France)

  • Fabrice Mériaudeau

    (ImViA EA 7535 and ERL VIBOT 6000, Université de Bourgogne, 21078 Dijon, France)

Abstract

The world faces difficulties in terms of eye care, including treatment, quality of prevention, vision rehabilitation services, and scarcity of trained eye care experts. Early detection and diagnosis of ocular pathologies would enable forestall of visual impairment. One challenge that limits the adoption of computer-aided diagnosis tool by ophthalmologists is the number of sight-threatening rare pathologies, such as central retinal artery occlusion or anterior ischemic optic neuropathy, and others are usually ignored. In the past two decades, many publicly available datasets of color fundus images have been collected with a primary focus on diabetic retinopathy, glaucoma, age-related macular degeneration and few other frequent pathologies. To enable development of methods for automatic ocular disease classification of frequent diseases along with the rare pathologies, we have created a new Retinal Fundus Multi-disease Image Dataset (RFMiD). It consists of 3200 fundus images captured using three different fundus cameras with 46 conditions annotated through adjudicated consensus of two senior retinal experts. To the best of our knowledge, our dataset, RFMiD, is the only publicly available dataset that constitutes such a wide variety of diseases that appear in routine clinical settings. This dataset will enable the development of generalizable models for retinal screening.

Suggested Citation

  • Samiksha Pachade & Prasanna Porwal & Dhanshree Thulkar & Manesh Kokare & Girish Deshmukh & Vivek Sahasrabuddhe & Luca Giancardo & Gwenolé Quellec & Fabrice Mériaudeau, 2021. "Retinal Fundus Multi-Disease Image Dataset (RFMiD): A Dataset for Multi-Disease Detection Research," Data, MDPI, vol. 6(2), pages 1-14, February.
  • Handle: RePEc:gam:jdataj:v:6:y:2021:i:2:p:14-:d:492119
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/6/2/14/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2306-5729/6/2/14/
    Download Restriction: no
    ---><---

    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:gam:jdataj:v:6:y:2021:i:2:p:14-:d:492119. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.