| 【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. |
