A lung sound recognition model to diagnoses the respiratory diseases by using transfer learning.

Respiratory disease is one of the leading causes of death in the world. Through advances in Artificial Intelligence, it appears possible for the days of misdiagnosis and treatment of respiratory disease symptoms rather than their root cause to move behind us. The traditional convolutional neural network cannot extract the temporal features of lung sounds. To solve the problem, a lung sounds recognition algorithm based on VGGish- stacked BiGRU is proposed which combines the VGGish network with the stacked bidirectional gated recurrent unit neural network. A lung Sound Recognition Algorithm Based on VGGish-Stacked BiGRU is used as a feature extractor which is a pre-trained model used for transfer learning. The target model is built with the same structure as the source model which is the VGGish model and parameter transfer is done from the source model to the target model. The multi-layer BiGRU stack is used to enhance the feature value and retain the model. While fine-tuning of the parameter of VGGish is frozen which successfully improves the model. The experimental results show that the proposed algorithm improves the recognition accuracy of lung sounds and the recognition accuracy of respiratory diseases. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Citation

Kumari Nidhi Lal. A lung sound recognition model to diagnoses the respiratory diseases by using transfer learning. Multimedia tools and applications. 2023 Mar 29:1-17

PMID: 37362727

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