CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive Learning
Journal:
arXiv
Published Date:
Feb 2, 2025
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 facilitating 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 38M, comparing 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.
