<ul><li>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.</li><li>The proposed model achieved 97% accuracy in detecting COVID-19 from nasal breathing sounds.</li><li>The Dense-ReLU-Dropout model, using RF and PCA for feature selection, achieves high accuracy with greater computational efficiency compared to existing methods.</li><li>The findings suggest that the proposed method holds significant potential for clinical implementation, advancing smartphone-based diagnostics in infectious diseases.</li></ul>

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

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