Morphologic identification of clinically encountered moulds using a residual neural network

Morphologic identification of clinically encountered moulds using a residual neural network

The use of morphology to diagnose invasive mould infections in China still faces substantial challenges, which often leads to delayed diagnosis or misdiagnosis. We developed a model called XMVision Fungus AI to identify mould infections by training, testing, and evaluating a ResNet-50 model. Our res...

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Bibliographic Details
Main Authors: Ran Jing, Xiang-Long Yin, Xiu-Li Xie, He-Qing Lian, Jin Li, Ge Zhang, Wen-Hang Yang, Tian-Shu Sun, Ying-Chun Xu
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-10-01
Series:Frontiers in Microbiology
Subjects:
ResNet-50
XMVision Fungus AI
clinical moulds
rapid identification
morphology
Online Access:https://www.frontiersin.org/articles/10.3389/fmicb.2022.1021236/full

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https://www.frontiersin.org/articles/10.3389/fmicb.2022.1021236/full

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