Deep recurrent neural networks with attention mechanisms for respiratory anomaly classification.

Wall, Conor; Zhang, Li; Yu, Yonghong; Mistry, Kamlesh

Abstract

In recent years, a variety of deep learning techniques and methods have been adopted to provide AI solutions to issues within the medical field, with one specific area being audio-based classification of medical datasets. This research aims to create a novel deep learning architecture for this purpose, with a variety of different layer structures implemented for undertaking audio classification. Specifically, bidirectional Long Short-Term Memory (BiLSTM) and Gated Recurrent Units (GRU) networks in conjunction with an attention mechanism, are implemented in this research for chronic and non-chronic lung disease and COVID-19 diagnosis. We employ two audio datasets, i.e. the Respiratory Sound and the Coswara datasets, to evaluate the proposed model architectures pertaining to lung disease classification. The Respiratory Sound Database contains audio data with respect to lung conditions such as Chronic Obstructive Pulmonary Disease (COPD) and asthma, while the Coswara dataset contains coughing audio samples associated with COVID-19. After a comprehensive evaluation and experimentation process, as the most performant architecture, the proposed attention BiLSTM network (A-BiLSTM) achieves accuracy rates of 96.2% and 96.8% for the Respiratory Sound and the Coswara datasets, respectively. Our research indicates that the implementation of the BiLSTM and attention mechanism was effective in improving performance for undertaking audio classification with respect to various lung condition diagnoses.

Citation

WALL, C., ZHANG, L., YU, Y. and MISTRY, K. 2021. Deep recurrent neural networks with attention mechanisms for respiratory anomaly classification. In Proceedings of 2021 International joint conference on neural networks (IJCNN 2021), 18-22 July 2021, [virtual conference]. Piscataway: IEEE [online], article 9533966. Available from: https://doi.org/10.1109/IJCNN52387.2021.9533966

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2021 International joint conference on neural networks (IJCNN 2021)
• Start Date: Jul 18, 2021
• End Date: Jul 22, 2021
• Acceptance Date: Apr 10, 2021
• Online Publication Date: Jul 22, 2021
• Publication Date: Sep 20, 2021
• Deposit Date: Sep 27, 2021
• Publicly Available Date: Sep 28, 2021
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Series ISSN: 2161-4407
• Book Title: Proceedings of 2021 Internationa joint confernce on neural networks (IJCNN 2021)
• ISBN: 9780738133669
• DOI: https://doi.org/10.1109/ijcnn52387.2021.9533966
• Keywords: Deep learning; Long short-term memory; Audio classification; Lung disease; COVID; Bidirectional recurrent neural network; Attention mechanism; COVID-19
• Public URL: https://rgu-repository.worktribe.com/output/1465465