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New research · Ophthalmology
Frontiers in artificial intelligence · 5d
StudyFrontiers in artificial intelligence · 2026

Fusion of ConvNeXt-Tiny and Swin-Tiny backbones: a comparative analysis for diabetic retinopathy classification.

J Paranthaman, Sathya Pichandi, Aparna Mohanty
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OphthalmologyStudy

A fusion model accurately classified diabetic retinopathy with 88.34% mean test accuracy.

Fusion of ConvNeXt-Tiny and Swin-Tiny backbones: a comparative analysis for diabetic retinopathy classification.

J Paranthaman … Aparna Mohanty
Frontiers in artificial intelligence · 2026
Background

Diabetic retinopathy (diabetic retinopathy) is a leading cause of preventable blindness, which has motivated the development of reliable automated grading systems on retinal fundus images.

Results
88.34%
the computer model correctly identified diabetic eye disease in this percentage of cases
More results

In this study, we perform a controlled comparative evaluation of ConvNeXt-Tiny, Swin-Tiny and their feature fusion for diabetic retinopathy classification using the Asia Pacific Tele-Ophthalmology Society (Asia Pacific Tele-Ophthalmology Society) 2019 dataset.

More results

All models were initialized with weights pre-trained on ImageNet-1K and evaluated with two transfer learning strategies: direct fine-tuning on Asia Pacific Tele-Ophthalmology Society 2019, and EyePACS-based domain adaptation with task-specific fine-tuning.

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Conclusion

The study also emphasizes the importance of controlled comparative evaluation, stability analysis, and configuration-specific evaluation in the research of medical image classification.

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