Deep learning-based automated detection for diabetic retinopathy and diabetic macular oedema in retinal fundus photographs.

ABSTRACT:

Objectives

To present and validate a deep ensemble algorithm to detect diabetic retinopathy (DR) and diabetic macular oedema (DMO) using retinal fundus images.

Methods

A total of 8739 retinal fundus images were collected from a retrospective cohort of 3285 patients. For detecting DR and DMO, a multiple improved Inception-v4 ensembling approach was developed. We measured the algorithm's performance and made a comparison with that of human experts on our primary dataset, while its generalization was assessed on the publicly available Messidor-2 dataset. Also, we investigated systematically the impact of the size and number of input images used in training on model's performance, respectively. Further, the time budget of training/inference versus model performance was analyzed.

SUBMITTER: Li F 

PROVIDER: S-EPMC9232645 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature