Diagnostic Study of Pneumonia Based on Voice Feature Recognition

Introduction: Deep learning-driven acoustic feature recognition significantly advances non-invasive disease screening. However, existing pneumonia detection methods based on cough sounds remain limited in terms of accuracy and robustness in complex acoustic environments. To address these challenges, we propose the Heterogeneous Cross-modal Topology Enhanced Network (HCTEN), which incorporates advanced attention mechanisms and convolutional strategies to capture diagnostically relevant, multi-scale acoustic events effectively. Methods: To address these limitations, we propose the Heterogeneous Cross-modal Topology Enhanced Network (HCTEN), which is introduced in the context of cough-based pneumonia classification. HCTEN integrates a Channel-Spatial Joint Attention Module (CSJAM) to emphasize diagnostic frequency bands and a Step Multi-scale Topological Convolution (SMTConv) to capture rich temporal-spectral representations. Results: Experiments conducted on 2,039 high-quality cough samples (cough score > 0.9) from the COUGHVID dataset demonstrate that HCTEN achieves 96.09% accuracy, outperforming state-of-the-art models with an average gain of 1.75% in accuracy, 5.42% in AUROC, 7.21% in F1-score, and 9.1% in sensitivity. Ablation studies further validate the contribution of each module. Discussion: Experimental results confirm that HCTEN benefits from the integration of CSJAM and SMT-Conv, which enhances the model’s ability to detect weak pathological acoustic features. These components significantly improve robustness under complex and noisy conditions, making the model suitable for real-world pneumonia screening scenarios. Conclusion: HCTEN provides a substantial advancement in pneumonia detection via acoustic cough analysis, reliably identifying pathological acoustic biomarkers. This framework holds significant potential for practical deployment in real-world intelligent diagnostic applications.