ArticleScientific reports2025
Optimising deep learning models for ophthalmological disorder classification.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
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Who cites it
6 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Healthcare analytics perspectives on artificial intelligence-based retinopathy of prematurity screening: a bibliometric mapping study.Frontiers in research metrics and analytics · 2026Pooled it
- Advances in AI-Driven EEG Analysis for Neurological and Oculomotor Disorders: A Systematic Review.Biosensors · 2025Pooled it
- ODDM: Integration of SMOTE Tomek with Deep Learning on Imbalanced Color Fundus Images for Classification of Several Ocular Diseases.Journal of imaging · 2025Article
- A Lightweight CNN for Multiclass Retinal Disease Screening with Explainable AI.Journal of imaging · 2025Article
- Deep learning-based CAD system for Alzheimer's diagnosis using deep downsized KPLS.Scientific reports · 2025Article
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Authors and funding
6 authors.
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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%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.