OSCR

Early retinal disease detection from fundus images using deep neural networks.

Overview

Authors: Abenet Alazar Hailu1, Esubalew Asmare Desta1, Atsedemaryam Mulugeta1, Fikadu Berie Adugna2, Ayodeji Olalekan Salau3,4, Lamesgin Addis Almaw5, Melsew Belachew Fentahun6
ORCID iDs: Ayodeji Olalekan Salau
  1. Department of Information Technology, College of Informatics, University of Gondar, Gondar, Ethiopia
  2. Department of Computer Science, College of Informatics, University of Gondar, Gondar, Ethiopia
  3. Department of Electrical/Electronics and Computer Engineering, Afe Babalola University, Ado-Ekiti, Nigeria
  4. Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu India
  5. Digital Transformation and Innovation, Department of Information Technology, College of Informatics, University of Gondar, Gondar, Ethiopia
  6. Department of Information Science, College of Informatics, University of Gondar, Gondar, Ethiopia
Institutions: University of Gondar (Ethiopia); Afe Babalola University (Nigeria); Saveetha University (India)
Journal: Scientific reports, volume 16, issue 1, article 17773
Dates: received 30 September 2025; accepted 24 February 2026; published online 16 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-42059-0 · PMID 41991556 · PMCID PMC13246789 · OpenAlex W7154575147
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: other (modality), human (organism), clinical / translational (subfield)
Methods: Machine learning, Statistics
Keywords: Deep learning, Fundus images, Retinal disease, DenseNet201, Automated screening, Convolutional neural networks, Computational biology and bioinformatics, Diseases, Health care, Mathematics and computing, Medical research
MeSH: Deep Learning*, Fundus Oculi*, Image Interpretation, Computer-Assisted*, Neural Networks, Computer*, Retinal Diseases*, Convolutional Neural Networks, Early Diagnosis, Humans, Image Processing, Computer-Assisted (* major topic)
Topic: Retinal Imaging and Analysis (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Citations: not cited yet (Europe PMC); 57 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Code

No file of the authors' code could be read here: it is described below, and read at its source.

drive.google.com/file/d

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)
  • 26 September 2026: the link answers (HTTP 200)
At the source: drive.google.com/file/d/1EyxpomtXip2Q04Dq7w-2pYgw0_QNgWL5/view

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 0 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41598-026-42059-0.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 11 keywords, 9 MeSH terms, 39 references.

Cite

This paper

Hailu, A. A., Desta, E. A., Mulugeta, A., Adugna, F. B., Salau, A. O., Almaw, L. A., & Fentahun, M. B. (2026). Early retinal disease detection from fundus images using deep neural networks. Scientific reports, 16(1), 17773. https://doi.org/10.1038/s41598-026-42059-0

BibTeX

@article{hailu2026early,
author = {Hailu, Abenet Alazar and Desta, Esubalew Asmare and Mulugeta, Atsedemaryam and Adugna, Fikadu Berie and Salau, Ayodeji Olalekan and Almaw, Lamesgin Addis and Fentahun, Melsew Belachew},
title = {{Early retinal disease detection from fundus images using deep neural networks}},
journal = {Scientific reports},
year = {2026},
month = apr,
volume = {16},
number = {1},
pages = {17773},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-42059-0},
url = {https://doi.org/10.1038/s41598-026-42059-0},
pmid = {41991556},
pmcid = {PMC13246789}
}

RIS

TY - JOUR
AU - Hailu, Abenet Alazar
AU - Desta, Esubalew Asmare
AU - Mulugeta, Atsedemaryam
AU - Adugna, Fikadu Berie
AU - Salau, Ayodeji Olalekan
AU - Almaw, Lamesgin Addis
AU - Fentahun, Melsew Belachew
TI - Early retinal disease detection from fundus images using deep neural networks
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/04/16
VL - 16
IS - 1
SP - 17773
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-42059-0
UR - https://doi.org/10.1038/s41598-026-42059-0
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41598-026-42059-0",
"type": "article-journal",
"title": "Early retinal disease detection from fundus images using deep neural networks",
"container-title": "Scientific reports",
"author": [
{
"family": "Hailu",
"given": "Abenet Alazar"
},
{
"family": "Desta",
"given": "Esubalew Asmare"
},
{
"family": "Mulugeta",
"given": "Atsedemaryam"
},
{
"family": "Adugna",
"given": "Fikadu Berie"
},
{
"family": "Salau",
"given": "Ayodeji Olalekan"
},
{
"family": "Almaw",
"given": "Lamesgin Addis"
},
{
"family": "Fentahun",
"given": "Melsew Belachew"
}
],
"container-title-short": "Sci Rep",
"volume": "16",
"issue": "1",
"page": "17773",
"DOI": "10.1038/s41598-026-42059-0",
"PMID": "41991556",
"PMCID": "PMC13246789",
"ISSN": "2045-2322",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41598-026-42059-0",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
16
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1371/journal.pone.0348866 [code]
Using deep learning to identify inherited retinal diseases based on wide-field retinal imaging data.
Journal: PloS one
In common: other, clinical / translational, 1 reference
[2] doi:10.1371/journal.pone.0347867 [code]
LiteFeatNet: A parameter-efficient and performance-centric deep learning model for multi-ocular disease identification using intermediate feature reduction from fundus images.
Journal: PloS one
In common: other, 1 reference
[3] doi:10.1371/journal.pone.0350637 [code]
Deep learning-based arterial waveform analysis for predicting postoperative cerebrovascular events in pediatric patients with Moyamoya disease.
Journal: PloS one
In common: clinical / translational, 1 reference
[4] doi:10.3389/fmed.2026.1810860
Lightweight deep learning for medical imaging using MobileNetV2-based brain pathology classification with Grad-CAM interpretability.
Journal: Frontiers in medicine
In common: other, 1 reference
[5] doi:
NeuroTrustNet: a cost-effective multimodal ensemble framework for brain tumor classification under cross-dataset variability
Journal: Frontiers in artificial intelligence
In common: 1 reference

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.