DeepFusion: early diagnosis of COPD, asthma, and pneumonia using lung sound analysis with a multimodal BiGRU network.

The key component of pulmonary disease is the structure of respiratory sound (RS) auscultation and its analysis, which provide symptomatic information about a patient's lung. The overlap in symptoms complicates early diagnosis, making timely and accurate differentiation essential for effective treatment. This study aims to develop a multimodal framework for distinguishing and early diagnosis of COPD, asthma, and pneumonia. Descriminative features are extracted from pre-processed lung sound signal using FBSE, Spectrogram, and MFCCs. These features are integrated through a weighted multimodal fusion method and classified using BiGRU network. The framework achieved 94.1% precision overall, with strong accuracy in pairwise disease distinction- 81.73%(COPD-Asthma), 94.41% (COPD- pneumonia), and 97.40%(Asthma- pneumonia).