Deep learning-based automated diagnosis of fungal keratitis with in vivo confocal microscopy images.

ABSTRACT:

Background

The aim of this study was to develop an intelligent system based on a deep learning algorithm for automatically diagnosing fungal keratitis (FK) in in vivo confocal microscopy (IVCM) images.

Methods

A total of 2,088 IVCM images were included in the training dataset. The positive group consisted of 688 images with fungal hyphae, and the negative group included 1,400 images without fungal hyphae. A total of 535 images in the testing dataset were not included in the training dataset. Deep Residual Learning for Image Recognition (ResNet) was used to build the intelligent system for diagnosing FK automatically. The system was verified by external validation in the testing dataset using the area under the receiver operating characteristic curve (AUC), accuracy, specific

SUBMITTER: Lv J 

PROVIDER: S-EPMC7327373 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature