Application of a deep learning system in glaucoma screening and further classification with colour fundus photographs: a case control study.

ABSTRACT:

Background

To verify efficacy of automatic screening and classification of glaucoma with deep learning system.

Methods

A cross-sectional, retrospective study in a tertiary referral hospital. Patients with healthy optic disc, high-tension, or normal-tension glaucoma were enrolled. Complicated non-glaucomatous optic neuropathy was excluded. Colour and red-free fundus images were collected for development of DLS and comparison of their efficacy. The convolutional neural network with the pre-trained EfficientNet-b0 model was selected for machine learning. Glaucoma screening (Binary) and ternary classification with or without additional demographics (age, gender, high myopia) were evaluated, followed by creating confusion matrix and heatmaps. Area under receiver operating charact

SUBMITTER: Hung KH 

PROVIDER: S-EPMC9743575 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature