A Hybrid System for Automatic Identification of Corneal Layers on In Vivo Confocal Microscopy Images.

ABSTRACT:

Purpose

Accurate identification of corneal layers with in vivo confocal microscopy (IVCM) is essential for the correct assessment of corneal lesions. This project aims to obtain a reliable automated identification of corneal layers from IVCM images.

Methods

A total of 7957 IVCM images were included for model training and testing. Scanning depth information and pixel information of IVCM images were used to build the classification system. Firstly, two base classifiers based on convolutional neural networks and K-nearest neighbors were constructed. Second, two hybrid strategies, namely weighted voting method and light gradient boosting machine (LightGBM) algorithm were used to fuse the results from the two base classifiers and obtain the final classification. Finally, the conf

SUBMITTER: Tang N 

PROVIDER: S-EPMC10108728 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature