ArticleDigital health
Disease-specific data augmentation enhances deep learning classification of age-related macular degeneration, diabetic retinopathy, and glaucoma.
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Abstract
Objective: To determine which data augmentation technique yields the best performance for deep learning models in classifying age-related macular degeneration (AMD), diabetic retinopathy (DR), glaucoma, and normal fundus images. Methods: This study employed an Results: The results from this study show that data augmentation consistently increased the area under the curve (AUC) from 96.55% to 97.23% and accuracy from 85.83% (baseline) to 89.58%. The results indicate that augmentation effectiveness is disease-specific: Rotation and Colour Jitter yielded the highest sensitivity for AMD (99%), CLAHE maximised sensitivity for Diabetic Retinopathy (96%), and Translation was most effective for Glaucoma (83%). While single augmentations provided descriptive clinical improvements, the comprehensive combination of photometric, geometric, and noise augmentations yielded the best overall performance and achieved a statistically significant improvement over the baseline (Mean bootstrapped AUC = 0.9800, 95% CI: 0.9678, 0.9895; Conclusion: Data augmentation effectiveness is disease-dependent; specific pathologies respond better to distinct augmentation techniques due to different retinal biomarkers.
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