A Deep Learning-Based Framework for Retinal Disease Classification

Amit Choudhary, Savita Ahlawat, Shabana Urooj, Nitish Pathak, A. Lay-Ekuakille, Neelam Sharma

Healthcare 2023 article cited by 103

Source Open-access PDF More in Medicine & Health

importance 6/10 solved Retinal Imaging and Analysis ophthalmology stage: research

Problem

Automatic detection and classification of retinal disease states from OCT images was needed to assist diagnosis.

Why it matters

Retinal conditions such as macular edema and choroidal neovascularization can cause vision loss if not detected early.

What the AI did

A customized 19-layer VGG-19 CNN with transfer learning classified 84,568 OCT images into four retinal conditions.

Results

99.17% classification accuracy, 0.995 specificity, 0.99 sensitivity on 84,568 OCT images; claimed better than existing models.

Impact

High benchmark accuracy on public OCT datasets; clinical adoption or external validation not stated.

Record

Venue	Healthcare
Published	2023
Work type	article
Authors	Amit Choudhary, Savita Ahlawat, Shabana Urooj, Nitish Pathak, A. Lay-Ekuakille, Neelam Sharma
Citations	103 (OpenAlex, at import time)
References	44
Model or method	VGG-19 (transfer learning)
Organization	not stated in source
Primary source	https://doi.org/10.3390/healthcare11020212
Open access	gold
Keywords	deep learning, VGG-19, OCT imaging, retinal disease, transfer learning, classification

Importance rating

6 / 10

significant — thousands to millions affected

Automatic detection of retinal disease states (CNV, drusen, diabetic macular edema) could assist diagnosis of vision-threatening conditions; scale not quantified in abstract.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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