Lung Sound Classification Using Co-tuning and Stochastic Normalization

• URL: http://arxiv.org/abs/2108.01991v1
• Date: Wed, 4 Aug 2021 12:16:02 GMT
• Title: Lung Sound Classification Using Co-tuning and Stochastic Normalization
• Authors: Truc Nguyen, Franz Pernkopf
• Abstract summary: The knowledge of the pre-trained model is transferred by using vanilla fine-tuning, co-tuning, normalization and the combination of the co-tuning and normalization techniques. 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.
• Score: 26.399917342840265
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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