Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 7, Issue 3 (May-June 2026) Submit your research before last 3 days of June to publish your research paper in the issue of May-June.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 7 Issue 3
May-June 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar Wiki for Call for Papers WorldCat Zenodo

Machine Learning–Based Fundus Image Analysis for Early Diagnosis of Diabetic Retinopathy: A Comprehensive Systematic Review

Author(s) Shubhangi Lanjewar, Vijaya Balpande, Bhagyashree Dharaskar
Country India
Abstract Diabetic retinopathy (DR) culminates to be the main reason of visual impairments that can be avoided. The early diagnosis by means of regular screenings turns out to be extremely important in the reduction of permanent eyesight loss risk. Color fundus photography is commonly used for the DR screening, but manual assessment is time-consuming and depends on the availability of the trained specialists. To address these limitations, automated analysis of the fundus images has been widely studied. This review presents the structured synthesis of the learning-based methods for DR grading, and lesion analysis using the fundus images. The literature covers traditional feature-based techniques, deep learning models, hybrid,
and transfer learning frameworks, and lesion-level segmentation approaches. The common datasets, preprocessing methods, model designs, and evaluation metrics are reviewed to identify the key trends, and methodological differences. Evidence from more than fifty literature studies indicates that the deep convolutional models, and the transfer learning approaches achieve higher accuracy than the conventional methods on benchmark datasets. Detection and segmentation of lesions certainly enhance the clinical interpretability but they do come with the limitation of required annotation efforts and data variability. The major issues that are still to be resolved are the diversity of datasets, unequal distribution of classes, poor external validation, and challenges in incorporating the results into the clinical setting. Progress in standardization, privacy-aware learning, and multimodal analysis is essential for the reliable as well as clinically usable DR screening systems.
Keywords Deep learning, diabetic retinopathy, fundus imaging, machine learning, medical image analysis, screening etc.
Published In Volume 7, Issue 2, March-April 2026
Published On 2026-04-24
DOI https://doi.org/10.63363/aijfr.2026.v07i02.5180
View / Download PDF File

Share this



E-ISSN 3048-7641
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

AIJFR DOI prefix is
10.63363/aijfr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 7 Isu 2Cover Page Vol 7 Isu 1Cover Page Vol 6 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About AIJFR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9898-79-39-59 editor@aijfr.com