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

A Comprehensive Study of Deep Learning Techniques for Retinal Disorders Screening Using Fundus Images

Author(s) Shivanjali Nimbalkar, Gyankamal Chhajed
Country India
Abstract Retinal diseases such as age-related macular disorders, diabetic retinopathy [1], and glaucoma areprogressive eye conditions that remain among the leading causes of preventable blindness worldwide.Initial detection and correct classification of these disorders are essential for timely treatment and visionpreservation. Now a days, deep learning has emerged as a transformative approach for analysing retinalfundus images, offering high accuracy and efficiency in automated diagnosis and grading [1, 6]. Thissurvey explores the latest advancements in DL-based classification of multiple retinal diseases, focusingon key architectures such as CNNs, RNNs, DBNs, and autoencoders [4, 15]. It also reviews widely usedpublic datasets such as EyePACS, APTOS 2019, and MESSIDOR [16], and discusses pre-processingtechniques including image enhancement, de-noising, and data augmentation [12, 13]. Furthermore, thepaper highlights the role of transfer learning and hybrid models in improving performance when labelledmedical data is limited [5, 7]. Challenges such as data imbalance, lack of image quality, and ethicalfactor in clinical adoption are addressed. Finally, future directions are outlined, including thedevelopment of lightweight models for mobile deployment [1], improved dataset diversity [13, 16], andstronger collaboration between AI researchers and healthcare professionals to reduce the gap between
research and real-world healthcare applications.
Keywords Retinal Disease, Multi-Disease Classification, Fundus Images, Convolutional Neural Networks (CNN), Transfer Learning, Image Pre-processing, Medical Image Analysis, Public Datasets (EyePACS, APTOS, MESSIDOR), Artificial Intelligence in Healthcare, Retinal Disease Screening Systems, Model Interpretability.
Published In Volume 7, Issue 3, May-June 2026
Published On 2026-05-27
DOI https://doi.org/10.63363/aijfr.2026.v07i03.5774
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