ABSTRACT
Listening to lung sounds through auscultation is vital in examining the respiratory system for abnormalities. Automated analysis of lung auscultation sounds can be beneficial to the health systems in low-resource settings where there is a lack of skilled physicians. In this work, we propose a lightweight convolutional neural network (CNN) architecture to classify respiratory diseases from individual breath cycles using hybrid scalogram-based features of lung sounds. The proposed feature-set utilizes the empirical mode decomposition (EMD) and the continuous wavelet transform (CWT). The performance of the proposed scheme is studied using a patient independent train-validation-test set from the publicly available ICBHI 2017 lung sound dataset. Employing the proposed framework, weighted accuracy scores of 98.92% for three-class chronic classification and 98.70% for six-class pathological classification are achieved, which outperform well-known and much larger VGG16 in terms of accuracy by absolute margins of 1.10% and 1.11%, respectively. The proposed CNN model also outperforms other contemporary lightweight models while being computationally comparable.
