IDEAS home Printed from https://ideas.repec.org/a/bjc/journl/v12y2025i5p1467-1475.html
   My bibliography  Save this article

A Comprehensive Study on the Automatic Identification of Diabetic Retinopathy

Author

Listed:
  • Md. Sabbir Ejaz

    (Information and Communication Engineering, Noakhali Science and Technology University)

  • Nusrat Mahee

    (Information and Communication Engineering, Noakhali Science and Technology University)

  • Md. Harun Or Rashid

    (Department of Computer Science and Engineering, Kishoreganj University)

  • Md. Omar Faruq

    (Computer Science and Engineering, Bangladesh Army University of Engineering & Technology)

Abstract

An eye condition known as diabetic retinopathy is caused by a higher blood sugar ratio. It usually doesn’t have any symptoms at the early stage, but in the later stage, the symptoms are visible, and the patient can find it difficult to see and can have irritation in the eye, which may result in blindness. Early discovery of it can lessen the spread of the disease in the eyes. Historically, diabetic retinopathy diagnosis was manual due to a lack of computerized technology. Manual detection-based methods have low accuracy, are time-consuming, and have side effects. Many methods have been put forward by researchers in order to discover diabetic retinopathy, such as machine learning, deep learning, image processing, data science, etc. These methods have used different datasets, including MESSIDOR, Kaggle, FUNDUS, EyePACS, DIRETDB1, macular optical coherence tomography, etc. In this paper, some of these recent proposed methods to detect diabetic retinopathy have been reviewed. The datasets that were employed by different techniques and the attained outcomes are presented in this paper. Also, the challenges and future plans of those methods are described. The comparative analysis of those methods will be helpful for a more thorough understanding of the detection of the disease.

Suggested Citation

  • Md. Sabbir Ejaz & Nusrat Mahee & Md. Harun Or Rashid & Md. Omar Faruq, 2025. "A Comprehensive Study on the Automatic Identification of Diabetic Retinopathy," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 12(5), pages 1467-1475, May.
    • Handle: RePEc:bjc:journl:v:12:y:2025:i:5:p:1467-1475
    as

    Download full text from publisher

    File URL: https://www.rsisinternational.org/journals/ijrsi/digital-library/volume-12-issue-5/1467-1475.pdf
    Download Restriction: no
    File URL: https://rsisinternational.org/journals/ijrsi/articles/a-comprehensive-study-on-the-automatic-identification-of-diabetic-retinopathy/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ganjar Alfian & Muhammad Syafrudin & Norma Latif Fitriyani & Muhammad Anshari & Pavel Stasa & Jiri Svub & Jongtae Rhee, 2020. "Deep Neural Network for Predicting Diabetic Retinopathy from Risk Factors," Mathematics, MDPI, vol. 8(9), pages 1-19, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vincent O. Omwenga & Vaishnav Madhumati & Kumar Vinay & Sathyanarayan Srikanta & Navakanta Bhat, 2023. "Mathematical Modelling of Combined Intervention Strategies for the Management and Control of Plasma Glucose of a Diabetes Mellitus Patient: A System Dynamic Modelling Approach," Mathematics, MDPI, vol. 11(2), pages 1-17, January.
    2. Norma Latif Fitriyani & Muhammad Syafrudin & Siti Maghfirotul Ulyah & Ganjar Alfian & Syifa Latif Qolbiyani & Muhammad Anshari, 2022. "A Comprehensive Analysis of Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian Screening Scores for Diabetes Risk Assessment and Prediction," Mathematics, MDPI, vol. 10(21), pages 1-23, October.

    More about this item

    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:bjc:journl:v:12:y:2025:i:5:p:1467-1475. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Dr. Renu Malsaria (email available below). General contact details of provider: https://rsisinternational.org/journals/ijrsi/ .

    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.