The Analysis of Deep Learning-Based Methods for Identifying Diabetic Retinopathy


Year : 2024 | Volume : 13 | Issue : 03 | Page : 15-31
    By

    Tshetiz Dahal,

  • Suyash Saurabh,

  1. General Physician & Clinical Researcher, Department of General Medicine, 16 Lypnia St. Rivne, Lugansk State Medical University,, Ukraine
  2. Junior Resident, Department of Physiology, Rajendra Institute of Medical Sciences, Bariatu, Ranchi,, Jharkhand, India

Abstract

Diabetic retinopathy (DR) is a degenerative eye condition resulting from diabetes mellitus, where high blood glucose levels lead to lesions on the retina. This condition is considered the leading cause of blindness among working-age diabetic patients, particularly in developing countries. As the disease is irreversible, the treatment aims to preserve the patient’s current vision. Early detection is crucial for effective management of DR to maintain vision. One of the main challenges in DR detection is the significant time, cost, and effort required for manual diagnosis, which involves having an ophthalmologist review retinal fundus images. The latter also turns out to be more challenging, especially when the sickness is still in its early stages and the signs of the illness are less noticeable in the pictures. Deep learning algorithms have helped in the early identification of DR, and machine learning-based medical image analysis has demonstrated proficiency in evaluating retinal fundus pictures. To propose retinal fundus picture classification and detection, this study discusses and analyzes the most recent deep learning techniques in supervised, self-supervised, and Vision Transformer configurations. For example, a review and summary of the DR referable, non-referable, and proliferative classifications are provided. The study also covers the retinal fundus datasets for DR that are currently accessible and can be used for segmentation, classification, and detection tasks. Along with addressing several issues that require more research and analysis, the paper evaluates research gaps in the field of DR detection and categorization.

Keywords: Diabetes, retina, retinopathy, lesions, deep learning, diseases, biomedical imaging, diabetic retinopathy, diabetes mellitus, diabetic macular edema

[This article belongs to Research & Reviews : A Journal of Medical Science and Technology (rrjomst)]

How to cite this article:
Tshetiz Dahal, Suyash Saurabh. The Analysis of Deep Learning-Based Methods for Identifying Diabetic Retinopathy. Research & Reviews : A Journal of Medical Science and Technology. 2024; 13(03):15-31.
How to cite this URL:
Tshetiz Dahal, Suyash Saurabh. The Analysis of Deep Learning-Based Methods for Identifying Diabetic Retinopathy. Research & Reviews : A Journal of Medical Science and Technology. 2024; 13(03):15-31. Available from: https://journals.stmjournals.com/rrjomst/article=2024/view=182668


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Regular Issue Subscription Original Research
Volume 13
Issue 03
Received 03/09/2024
Accepted 09/09/2024
Published 13/11/2024


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