A self-adaptive deep learning method for automated eye laterality detection based on color fundus photography.

ABSTRACT:

Purpose

To provide a self-adaptive deep learning (DL) method to automatically detect the eye laterality based on fundus images.

Methods

A 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 characteristic curve (AUC) with sensitivity and specificity and confusion matrix were applied to assess the model performance. The class activation map (CAM) was used for model visualization.

Results

In the external validation (N = 2000, 50% labeled as left eye), the AUC of

SUBMITTER: Liu C 

PROVIDER: S-EPMC6752776 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature