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Demonstrates an ensemble deep learning model achieving 75% accuracy in quality estimation of diabetic retinopathy fundus images, indicating potential for automated clinical screening support.
deep neural network models is proposed. The ensemble method was cross-validated and tested on one of the largest openly available datasets, the Deep Diabetic Retinopathy Image Dataset (DeepDRiD). We obtained a test accuracy of 75% for the QE, outperforming the existing methods on the DeepDRiD. Hence, the proposed ensemble method may be a potential tool for automated QE of fundus images and could be handy to ophthalmologists.
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Tummala et al. (2023) studied this question.
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