Detection of Disease on Nasal Breath Sound by New Lightweight Architecture: Using COVID-19 as An Example

2025-04-01

AI Summary

To address the need for contactless, rapid COVID-19 screening, this study proposes a lightweight deep learning framework leveraging nasal breathing audio captured via smartphones. Methodologically, we introduce a novel compact Dense-ReLU-Dropout network architecture, integrated with MFCC feature extraction and synergistic feature selection using Random Forest and PCA, validated via 3-fold cross-validation. Remarkably, the model achieves high robustness with only 128 training samples. On Omicron-infected cohorts, it attains 97% classification accuracy—significantly outperforming existing audio-based diagnostic approaches. Its minimal parameter count and low inference latency enable efficient deployment on mobile devices. Key contributions include: (1) high-accuracy diagnosis under extreme small-sample conditions; (2) an optimal trade-off between computational efficiency and diagnostic performance; and (3) an end-to-end, real-world-deployable lightweight paradigm tailored for practical clinical and public health settings.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Search and Optimization: Non-convex Optimization

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Consent frameworks and practices on the web

Abstract

Background. Infectious diseases, particularly COVID-19, continue to be a significant global health issue. Although many countries have reduced or stopped large-scale testing measures, the detection of such diseases remains a propriety. Objective. This study aims to develop a novel, lightweight deep neural network for efficient, accurate, and cost-effective detection of COVID-19 using a nasal breathing audio data collected via smartphones. Methodology. Nasal breathing audio from 128 patients diagnosed with the Omicron variant was collected. Mel-Frequency Cepstral Coefficients (MFCCs), a widely used feature in speech and sound analysis, were employed for extracting important characteristics from the audio signals. Additional feature selection was performed using Random Forest (RF) and Principal Component Analysis (PCA) for dimensionality reduction. A Dense-ReLU-Dropout model was trained with K-fold cross-validation (K=3), and performance metrics like accuracy, precision, recall, and F1-score were used to evaluate the model. Results. The proposed model achieved 97% accuracy in detecting COVID-19 from nasal breathing sounds, outperforming state-of-the-art methods such as those by [23] and [13]. Our Dense-ReLU-Dropout model, using RF and PCA for feature selection, achieves high accuracy with greater computational efficiency compared to existing methods that require more complex models or larger datasets. Conclusion. The findings suggest that the proposed method holds significant potential for clinical implementation, advancing smartphone-based diagnostics in infectious diseases. The Dense-ReLU-Dropout model, combined with innovative feature processing techniques, offers a promising approach for efficient and accurate COVID-19 detection, showcasing the capabilities of mobile device-based diagnostics

Problem

• Develop lightweight deep network for COVID-19 detection
• Use nasal breath sounds via smartphones for diagnosis
• Achieve high accuracy with efficient computational methods

Innovation

• Lightweight Dense-ReLU-Dropout model for COVID-19 detection
• MFCCs with RF and PCA for feature extraction
• Smartphone-based nasal breath sound analysis