CycleGuardian: a framework for automatic respiratory sound classification based on improved deep clustering and contrastive learning
Abstract Auscultation plays a pivotal role in early respiratory and pulmonary disease diagnosis. Despite the emergence of deep learning-based methods for automatic respiratory sound classification post-Covid-19, limited datasets impede performance enhancement. Distinguishing between normal and abnor...
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| Format: | Article |
| Language: | English |
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Springer
2025-03-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-025-01800-4 |
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| author | Yun Chu Qiuhao Wang Enze Zhou Ling Fu Qian Liu Gang Zheng |
| author_facet | Yun Chu Qiuhao Wang Enze Zhou Ling Fu Qian Liu Gang Zheng |
| author_sort | Yun Chu |
| collection | DOAJ |
| description | Abstract Auscultation plays a pivotal role in early respiratory and pulmonary disease diagnosis. Despite the emergence of deep learning-based methods for automatic respiratory sound classification post-Covid-19, limited datasets impede performance enhancement. Distinguishing between normal and abnormal respiratory sounds poses challenges due to the coexistence of normal respiratory components and noise components in both types. Moreover, different abnormal respiratory sounds exhibit similar anomalous features, hindering their differentiation. Besides, existing state-of-the-art models suffer from excessive parameter size, impeding deployment on resource-constrained mobile platforms. To address these issues, we design a lightweight network CycleGuardian and propose a framework based on an improved deep clustering and contrastive learning. We first generate a hybrid spectrogram for feature diversity and grouping spectrograms to facilitate intermittent abnormal sound capture. Then, CycleGuardian integrates a deep clustering module with a similarity-constrained clustering component to improve the ability to capture abnormal features and a contrastive learning module with group mixing for enhanced abnormal feature discernment. Multi-objective optimization enhances overall performance during training. In experiments, we use the ICBHI2017 dataset, following the official split method and without any pre-trained weights, our method achieves Sp: 82.06 $$\%$$ % , Se: 44.47 $$\%$$ % , and Score: 63.26 $$\%$$ % with a network model size of 38 M. Compared to the current model, our method leads by nearly 7 $$\%$$ % , achieving the current best performances. Additionally, we deploy the network on Android devices, showcasing a comprehensive intelligent respiratory sound auscultation system. |
| format | Article |
| id | doaj-art-0eb523ca5c244a54bc7ee4aa887f9e9a |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-0eb523ca5c244a54bc7ee4aa887f9e9a2025-08-20T03:40:44ZengSpringerComplex & Intelligent Systems2199-45362198-60532025-03-0111412010.1007/s40747-025-01800-4CycleGuardian: a framework for automatic respiratory sound classification based on improved deep clustering and contrastive learningYun Chu0Qiuhao Wang1Enze Zhou2Ling Fu3Qian Liu4Gang Zheng5School of Information and Communication, Hainan UniversitySchool of BioMedical Engineering, Hainan UniversitySchool of Electronic Information and Communications, Huazhong University of Science and TechnologySchool of BioMedical Engineering, Hainan UniversitySchool of BioMedical Engineering, Hainan UniversitySchool of Electronic Information and Communications, Huazhong University of Science and TechnologyAbstract Auscultation plays a pivotal role in early respiratory and pulmonary disease diagnosis. Despite the emergence of deep learning-based methods for automatic respiratory sound classification post-Covid-19, limited datasets impede performance enhancement. Distinguishing between normal and abnormal respiratory sounds poses challenges due to the coexistence of normal respiratory components and noise components in both types. Moreover, different abnormal respiratory sounds exhibit similar anomalous features, hindering their differentiation. Besides, existing state-of-the-art models suffer from excessive parameter size, impeding deployment on resource-constrained mobile platforms. To address these issues, we design a lightweight network CycleGuardian and propose a framework based on an improved deep clustering and contrastive learning. We first generate a hybrid spectrogram for feature diversity and grouping spectrograms to facilitate intermittent abnormal sound capture. Then, CycleGuardian integrates a deep clustering module with a similarity-constrained clustering component to improve the ability to capture abnormal features and a contrastive learning module with group mixing for enhanced abnormal feature discernment. Multi-objective optimization enhances overall performance during training. In experiments, we use the ICBHI2017 dataset, following the official split method and without any pre-trained weights, our method achieves Sp: 82.06 $$\%$$ % , Se: 44.47 $$\%$$ % , and Score: 63.26 $$\%$$ % with a network model size of 38 M. Compared to the current model, our method leads by nearly 7 $$\%$$ % , achieving the current best performances. Additionally, we deploy the network on Android devices, showcasing a comprehensive intelligent respiratory sound auscultation system.https://doi.org/10.1007/s40747-025-01800-4Intelligent auscultationDeep learningContrastive learningRespiratory sound |
| spellingShingle | Yun Chu Qiuhao Wang Enze Zhou Ling Fu Qian Liu Gang Zheng CycleGuardian: a framework for automatic respiratory sound classification based on improved deep clustering and contrastive learning Complex & Intelligent Systems Intelligent auscultation Deep learning Contrastive learning Respiratory sound |
| title | CycleGuardian: a framework for automatic respiratory sound classification based on improved deep clustering and contrastive learning |
| title_full | CycleGuardian: a framework for automatic respiratory sound classification based on improved deep clustering and contrastive learning |
| title_fullStr | CycleGuardian: a framework for automatic respiratory sound classification based on improved deep clustering and contrastive learning |
| title_full_unstemmed | CycleGuardian: a framework for automatic respiratory sound classification based on improved deep clustering and contrastive learning |
| title_short | CycleGuardian: a framework for automatic respiratory sound classification based on improved deep clustering and contrastive learning |
| title_sort | cycleguardian a framework for automatic respiratory sound classification based on improved deep clustering and contrastive learning |
| topic | Intelligent auscultation Deep learning Contrastive learning Respiratory sound |
| url | https://doi.org/10.1007/s40747-025-01800-4 |
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