Optimising deep learning models for ophthalmological disorder classification.

Vidivelli, S; Padmakumari, P; Parthiban, C; DharunBalaji, A; Manikandan, R; Gandomi, AH

Optimising deep learning models for ophthalmological disorder classification.

Vidivelli, S Padmakumari, P Parthiban, C DharunBalaji, A Manikandan, R Gandomi, AH

Permalink

Publisher:: NATURE PORTFOLIO
Publication Type:: Journal Article
Citation:: Sci Rep, 2025, 15, (1), pp. 3115
Issue Date:: 2025-01-24

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Download Published versionAdobe PDF (2.01 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Vidivelli, S
dc.contributor.author	Padmakumari, P
dc.contributor.author	Parthiban, C
dc.contributor.author	DharunBalaji, A
dc.contributor.author	Manikandan, R
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2025-03-19T23:56:59Z
dc.date.available	2024-10-08
dc.date.available	2025-03-19T23:56:59Z
dc.date.issued	2025-01-24
dc.identifier.citation	Sci Rep, 2025, 15, (1), pp. 3115
dc.identifier.issn	2045-2322
dc.identifier.issn	2045-2322
dc.identifier.uri	http://hdl.handle.net/10453/186004
dc.description.abstract	Fundus imaging, a technique for recording retinal structural components and anomalies, is essential for observing and identifying ophthalmological diseases. Disorders such as hypertension, glaucoma, and diabetic retinopathy are indicated by structural alterations in the optic disc, blood vessels, fovea, and macula. Patients frequently deal with various ophthalmological conditions in either one or both eyes. In this article, we have used different deep learning models for the categorisation of ophthalmological disorders into multiple classes and multiple labels utilising transfer learning-based convolutional neural network (CNN) methods. The Ocular Disease Intelligent Recognition (ODIR) database is used for experiments, and it contains fundus images of the patient's left and right eyes. We compared the performance of two different optimisers, Stochastic Gradient Descent (SGD) and Adam, separately. The best result was achieved using the MobileNet model with the Adam optimiser, yielding a testing accuracy of 89.64%.
dc.format	Electronic
dc.language	eng
dc.publisher	NATURE PORTFOLIO
dc.relation.ispartof	Sci Rep
dc.relation.isbasedon	10.1038/s41598-024-75867-3
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.mesh	Deep Learning
dc.subject.mesh	Humans
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Eye Diseases
dc.subject.mesh	Fundus Oculi
dc.subject.mesh	Diabetic Retinopathy
dc.subject.mesh	Databases, Factual
dc.subject.mesh	Fundus Oculi
dc.subject.mesh	Humans
dc.subject.mesh	Eye Diseases
dc.subject.mesh	Diabetic Retinopathy
dc.subject.mesh	Databases, Factual
dc.subject.mesh	Deep Learning
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Deep Learning
dc.subject.mesh	Humans
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Eye Diseases
dc.subject.mesh	Fundus Oculi
dc.subject.mesh	Diabetic Retinopathy
dc.subject.mesh	Databases, Factual
dc.title	Optimising deep learning models for ophthalmological disorder classification.
dc.type	Journal Article
utslib.citation.volume	15
utslib.location.activity	England
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Data Science Institute (DSI)
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-03-19T23:56:55Z
pubs.issue	1
pubs.publication-status	Published online
pubs.volume	15
utslib.citation.issue	1

Abstract:

Fundus imaging, a technique for recording retinal structural components and anomalies, is essential for observing and identifying ophthalmological diseases. Disorders such as hypertension, glaucoma, and diabetic retinopathy are indicated by structural alterations in the optic disc, blood vessels, fovea, and macula. Patients frequently deal with various ophthalmological conditions in either one or both eyes. In this article, we have used different deep learning models for the categorisation of ophthalmological disorders into multiple classes and multiple labels utilising transfer learning-based convolutional neural network (CNN) methods. The Ocular Disease Intelligent Recognition (ODIR) database is used for experiments, and it contains fundus images of the patient's left and right eyes. We compared the performance of two different optimisers, Stochastic Gradient Descent (SGD) and Adam, separately. The best result was achieved using the MobileNet model with the Adam optimiser, yielding a testing accuracy of 89.64%.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/186004