Abstract
Abstract
Diabetic retinopathy (DR) is caused by diabetes and is usually identified from retinal fundus images. Regular DR screening from digital fundus images could be burdensome to ophthalmologists and moreover prone to human errors. The quality of the fundus images is essential to improve the quality of the classification and thereby reduce diagnostic errors. Hence an automated method for quality estimation (QE) of digital fundus images using an ensemble of EfficientNetV2 models including small, medium, and large models is proposed. The ensemble method was cross-validated and tested on an openly available dataset from DeepDRiD. The test accuracy for QE is 75% outperforming the existing methods on the DeepDRiD dataset. Hence, this may be a potential tool for automated QE of fundus images and could be handy to the ophthalmologist.
Publisher
Research Square Platform LLC
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