Lung disease analysis using ensemble knowledge distillation

Since the outbreak of COVID-19, lung diseases have attracted more attention The characteristics and diagnosis of lung sounds become an important part of pulmonary pathology The existing works for lung sound analysis mainly aim to classify the types of abnormal lung sounds There are few studies focusing on the classification of lung diseases Moreover, a single classification model cannot take advantage of the train data from multiple sources due to privacy leakage concerns, and it is difficult for complex models to classify in real time Therefore, this paper proposes a model for classifying lung diseases based on ensemble knowledge distillation Firstly, Mel-spectrum features were extracted from lung sounds, and then multiple binary convolutional neural network models were established as teacher models Finally, a simplified multi-class student model will learn the knowledge of multiple teacher models through the technology of ensemble knowledge distillation Our experiments show that the student model reduces 79% of the parameters and improves the prediction efficiency by 20% than teacher model while achieving a predictive accuracy of 95% Under the same condition, the student model only incurs 6% of the time that is used by the state-of-the-art MobileNet-v3-small model Thus, our model has potential to be deployed in real world for real-time diagnosis of the lung diseases © 2021, Editorial Department of Control Theory & Applications South China University of Technology All right reserved