摘要
【目的】细菌耐药性监测是公共健康安全领域一项至关重要的工作。针对目前耐药性试验结果需要人工读取而结果判读主观性强且效率较低的问题,提出了利用卷积神经网络进行微孔图像识别的药敏试验结果判读方法。【方法】根据国家兽医微生物耐药性风险评估实验室提供的MIC试验图像构建数据集,利用Inception_V4和MobileNet_V1两个神经网络模型进行单个微孔药敏试验结果图像识别模型的训练,并基于模型判断的分类结果构建MIC值计算方法以及耐药性判断方法,实现药敏试验结果的自动识别。【结果】利用Inception_V4和MobileNet_V1两个神经网络模型进行单个微孔药敏试验结果图像识别的准确率分别达到99.99%、99.97%,MIC值及耐药性判定的准确率分别达到97.30%、94.40%和99.13%、98.40%。【结论】两种卷积神经网络均可替代人工判读,提高工作效率,并降低对实验人员的专业性要求。与Inception_V4相比,MobileNet_V1模型的判读精度略低,但效率较高,可达到实用化程度。
【Objective】Antimicrobial resistance surveillance is a vital task in the field of public health safety.Up to now,most results of antimicrobial susceptibility testing (AST) need to be recognized manually,which lead to subjective interpretations of experimental results and lower working efficiency.In this paper,an automatic method for interpreting AST results of microporous image recognition based on convolutional neural network is proposed.【Method】According to the data set of MIC test image construction provided by the National Veterinary Micromicrobial Resistance Risk Assessment Laboratory,the image recognition model of single microporous AST result was trained by,using two convolutional network models-Inception_V4 and MobileNet_V1.Based on the classification results of the model recognition,the MIC value calculation method and the drug resistance recognition method were established,and the automatic identification of the AST results was achieved.【Result】With the two Convolutional Neural Networks-Inception_V4 and MobileNet_V1Inception_V4,the accuracy rates of image recognition of single microporous AST results were 99.99% and 99.97%,respectively.And the accuracy of MIC value and drug resistance recognition reached 97.30%,94.40% and 99.13%,98.40%,respectively.【Conclusion】Both of the two convolutional neural networks can replace manual interpretation,to improve the work efficiency and reduce the professional requirements for experimenters.Compared with Inception_V4,the interpretation accuracy of the MobileNet_V1 model is slightly lower,but the efficiency is higher,and the practicality can be reached.
作者
郭玉彬
林欣颖
曾晓银
孙坚
李西明
GUO Yubin;LIN Xinying;ZENG Xiaoyin;SUN Jian;LI Ximing(College of Mathematics and Informatics,South China Agricultural University,Guangzhou 510642,China;College of Veterinary Medicine,South China Agricultural University,Guangzhou 510642,China)
出处
《广东农业科学》
CAS
2020年第2期141-148,共8页
Guangdong Agricultural Sciences
基金
国家重点研发计划项目(2016YFD0501300)
国家基金海外合作重点项目(30520103918)
广东省乡村振兴战略专项(粤农计〔2018〕54号)。
关键词
卷积神经网络
耐药性
药敏试验
最低抑菌浓度(MIC)
Convolutional Neural Network
drug resistance
antimicrobial susceptibility test
minimal inhibitory contentration(MIC)
