Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 28, 26 December 2023
Open
Access | Article
Using upsampling CONV-LSTM with metadata embedding for respiratory sound classification
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Abstract
Respiratory diseases are one of the leading causes of death around the world and they severely affect patient quality of life. Auscultation is an essential method for diagnosing respiratory diseases, and it is low-cost and convenient. However, auscultation requires experts who are highly experienced. Medical trainees suffer from misdiagnosis inevitably. To address this issue, a novel machine learning model is proposed, which consists of upsampling convolutional neural network (CNN), a long short-term memory network (LSTM), and a fully connected network (FCNN) with embedding layers to classify respiratory sounds into seven categories: Normal (N), Rhonchi (R), Wheeze (W), Stridor (S), Coarse Crackle (CC), Fine Crackle (FC), Wheeze & Crackle (WC). The model is trained and evaluated on the SPRSound dataset and obtained the result on the test dataset with a sensitivity of 0.5716, specificity of 0.7882, average score of 0.6799, harmonic score of 0.6626, and total score of 0.6756.
Keywords
respiratory sound classification, CNN, LSTM, upsampling, embedding, FCNN
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2023 International Conference on Mathematical Physics and Computational Simulation
- ISBN (Print)
- 978-1-83558-261-9
- ISBN (Online)
- 978-1-83558-262-6
- Published Date
- 26 December 2023
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/28/20230401
- Copyright
- 26 December 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited