Lung Sound Classification Using Co-tuning and Stochastic Normalization
Abstract
In this paper, we use pre-trained ResNet models as backbone architectures for classification of adventitious lung sounds and respiratory diseases. The knowledge of the pre-trained model is transferred by using vanilla fine-tuning, co-tuning, stochastic normalization and the combination of the co-tuning and stochastic normalization techniques. Furthermore, data augmentation in both time domain and time-frequency domain is used to account for the class imbalance of the ICBHI and our multi-channel lung sound dataset. Additionally, we apply spectrum correction to consider the variations of the recording device properties on the ICBHI dataset. Empirically, our proposed systems mostly outperform all state-of-the-art lung sound classification systems for the adventitious lung sounds and respiratory diseases of both datasets.
Cite
@article{arxiv.2108.01991,
title = {Lung Sound Classification Using Co-tuning and Stochastic Normalization},
author = {Truc Nguyen and Franz Pernkopf},
journal= {arXiv preprint arXiv:2108.01991},
year = {2021}
}
Comments
Submitted to IEEE BE Transaction