Deep Learning Based Algorithms for Detecting Chronic Obstructive Pulmonary Disease

Abstract

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease with various clinical presentations. The basic abnormality in all patients with COPD is airflow limitation. The main method for diagnosis of COPD is using spirometer and imaging equipment, which are expensive and not suitable for use. This study aims at developing algorithms for analysing cough sounds for detecting COPD. CNN and CRNN based deep learning techniques are used for developing the algorithm. We have used both augmented and non-augmented datasets with three different feature extraction methods: Mel-frequency cepstral coefficient, zero crossing rate, and harmonic change detection function. The developed CNN and CRNN scored an accuracy of 96.6% and 96.73% respectively. Conclusion: The proposed algorithms have improved classification performance that had been reported in the literature. Significance: The results of this study suggest that automatic diagnostic tools can be developed with less intervention from healthcare professionals.