Enhancing Deep Learning Performance for Diabetic Retinopathy Diagnosis Through Data Augmentation

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Key Points

This research aims to improve the performance of deep learning algorithms in diagnosing diabetic retinopathy using data augmentation techniques.
Utilized a deep learning model for diabetic retinopathy diagnosis.
Applied various data augmentation strategies to increase the diversity of training images.
Performed comparative analysis to assess the model's performance pre- and post-augmentation.
Deep learning model accuracy increased from 85% to 92% after applying data augmentation methods.
Sensitivity improved significantly from 80% to 88%, with a p-value of 0.002.
Specificity remained consistently high at 95% across both conditions.

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Shows improved diagnostic accuracy in diabetic retinopathy, suggesting effective data augmentation methods.

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Kaur et al. (2026) studied this question.

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