A self-adaptive deep learning method for automated eye laterality detection based on color fundus photography
Version 1PurposeTo provide a self-adaptive deep learning (DL) method to automatically detect the eye laterality based on fundus images.MethodsA total of 18394 fundus images with real-world eye laterality labels were used for model development and internal validation. A separate dataset of 2000 fundus images with eye laterality labeled manually was used for external validation. A DL model was developed based on a fine-tuned Inception-V3 network with self-adaptive strategy. The area under receiver operator
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PurposeTo provide a self-adaptive deep learning (DL) method to automatically detect the eye laterality based on fundus images.MethodsA total of 18394 fundus images with real-world eye laterality labels were used for model development and internal validation. A separate dataset of 2000 fundus images with eye laterality labeled manually was used for external validation. A DL model was developed based on a fine-tuned Inception-V3 network with self-adaptive strategy. The area under receiver operator