Construction of a rapid image recognition system for Staphylococcus aureus and Enterococcus faecalis based on deep learning
10.13602/j.cnki.jcls.2024.07.01
- VernacularTitle:基于深度学习构建金黄色葡萄球菌和粪肠球菌的快速图像识别系统
- Author:
Yuanmei LUO
1
;
Kewei CHEN
;
Zhenzhang LI
;
Yubiao YUE
;
Lingjuan CHEN
;
Jiawei LIU
;
Qiguang LI
;
Yang LI
;
Lingqing XU
Author Information
1. 广州医科大学附属清远医院/清远市人民医院检验医学部,广东清远 511518
- Keywords:
deep learning;
Gram staining;
Staphylococcus aureus;
Enterococcus faecalis;
bloodstream infection;
rapid identification
- From:
Chinese Journal of Clinical Laboratory Science
2024;42(7):481-487
- CountryChina
- Language:Chinese
-
Abstract:
Objective To identify the pathogenic bacteria such as Staphylococcus aureus and Enterococcus faecalis in bloodstream infec-tions with high confidence based on three deep learning models such as GoogleNet,ResNet101,and Vgg19,compare the performance and classification ability of these models,and explore the feasibility of applying the deep learning models for the rapid identification of pathogenic bacteria in bloodstream infections.Methods The preprocessed Gram-stained bacterial images,including 1 682 images for Staphylococcus aureus and 1 723 for Enterococcus faecalis,and 688 blank control microscopic images were input into three models for training and validation,respectively.Among them,1 344 images for Staphylococcus aureus,1 376 for Enterococcus faecalis,and 544 blank control images were used for training,and the remaining images were used for validation.The model with the best performance was identified according to the classification parameters between the models.Results The ResNet101 model had the lowest cross-en-tropy loss value(0.008 710 3),the largest Epoch value(93),and the highest accuracy rate(99％)for identifying the three types of validation set images.The cross-entropy loss value,Epoch value,and accuracy rate of the GoogleNet model were 0.063 89,86 and 98.6％,respectively,for identifying the three types of validation set images.Those of the Vgg19 model were 0.035 682,86 and 97.7％,respectively.Conclusion The ResNet101 model has the best performance in the classification of three kinds of images.The deep learning model may accurately,reliably and rapidly identify the Gram-stained images of pathogenic bacteria such as Staphylococcus aureus and Enterococcus faecalis in bloodstream infections.
