Cargando…
Deep learning-based automated diagnosis of fungal keratitis with in vivo confocal microscopy images
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 c...
Autores principales: | Lv, Jian, Zhang, Kai, Chen, Qing, Chen, Qi, Huang, Wei, Cui, Ling, Li, Min, Li, Jianyin, Chen, Lifei, Shen, Chaolan, Yang, Zhao, Bei, Yixuan, Li, Lanjian, Wu, Xiaohang, Zeng, Siming, Xu, Fan, Lin, Haotian |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7327373/ https://www.ncbi.nlm.nih.gov/pubmed/32617326 http://dx.doi.org/10.21037/atm.2020.03.134 |
Ejemplares similares
-
AI-Based Decision-Support System for Diagnosing Acanthamoeba Keratitis Using In Vivo Confocal Microscopy Images
por: Lincke, Alisa, et al.
Publicado: (2023) -
A Hybrid System for Automatic Identification of Corneal Layers on In Vivo Confocal Microscopy Images
por: Tang, Ningning, et al.
Publicado: (2023) -
Monitoring the Progression of Clinically Suspected Microbial Keratitis Using Convolutional Neural Networks
por: Kuo, Ming-Tse, et al.
Publicado: (2023) -
Fungal Keratitis – Improving Diagnostics by Confocal Microscopy
por: Nielsen, E., et al.
Publicado: (2013) -
Automated Disengagement Tracking Within an Intelligent Tutoring System
por: Chen, Su, et al.
Publicado: (2021)