Study of breath acetone detection based on D-GoogLeNet deep learning algorithm
For the detection of breath acetone in patients,this paper proposes a non-invasive breath detection method based on GoogLeNet deep learning framework combined with electronic nose(E-nose)sensors array.The proposed method not only overcome the traditional electronic nose breath testing shortcoming,manually extract the features in the process of data processing,but also innovatively converted the gas sensor response time series data into a response figure through image visualization methods,to realize the accurate identification of target gas in the gas mixture;at the same time,the existing GoogLeNet architecture was modified,the improved model(D-GoogLeNet)reduced the occurrence of overfitting phenomenon,and could achieve effective classification even with small sample sizes.In addition,in order to verify the robustness of the model,different concentrations of patients'breath markers are simulated,and the Gaussian noise is introduced,testing the anti interference ability of the model.The experimental results show that the classification accuracy,recall and precision of acetone,ethanol and their mixtures are all 1 without adding noise.When the standard deviation of noise is 100,the classification accuracy,precision and recall of the model for single gas is still 1,but the classification accuracy of the mixture is reduced to 0.84 and the precision and recall were reduced to 0.94.The experimental results proved the feasibility of the proposed method,which was expected to be a basis for clinical detection.
NETL
NSTL
万方数据
维普
