TriSpectraLSTM for COPD Detection via Lung Sound Analysis

Chronic Obstructive Pulmonary Disease is a progressive health condition characterized by restricted air-flow in the lungs, leading to life-threatening risks including loss of life. Detecting and diagnosing COPD early can significantly enhance disease management and patient well-being. However, existing diagnostic approaches for COPD are costly, time-intensive, and demand specialized equipment. Consequently, there is a pressing demand for an automated, accessible, and cost-effective diagnostic tool for diagnosing COPD. This work proposes a novel TriSpectraLSTM model that listens to the melody of lung sounds through multiple audio features, such as: Mel-frequency cepstral coefficients, chromagram, and Mel spectrograms. Each sub-model analyzes its assigned instrument, extracting distinct sonic signatures, before the hybrid network merges them, revealing the hidden COPD features. The model achieves a remarkable 93% accuracy on test data, surpassing existing methods. This multimodal approach by capturing the entire range of lung sounds, it produces a more precise and detailed diagnosis. This work employed the power of machine learning with healthcare, paving the way for a future where lung sounds become the key to diagnosing COPD diseases.