Lung Sound Assisted Diagnosis of Viral Pneumonia Based on Deep Learning
Aiming at the problems that the traditional lung sound recognition method has low accuracy and difficulty in adapt-ing to practical needs,a lung sound auxiliary diagnosis method based on deep learning is proposed.The deep learning model GoogLeNet-LSTM is designed to deeply analyze the spatial and time series features of the Mel feature maps to diagnose the condi-tion of patients with viral pneumonia.Compared with network models such as VGG16 and ResNet,the proposed model has better performance,with a diagnosis accuracy rate of 92.06%.The accuracy of the single model is improved by at least 4%.The experimen-tal results show that the network model can effectively support the evaluation of patients with viral pneumonia and play an auxiliary role in diagnosis.
万方数据
维普
