  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 using hybrid scalogram-based features of lung sounds. The hybrid
scalogram features utilize the empirical mode decomposition (EMD) and
continuous wavelet transform (CWT). The proposed scheme's performance is
studied using a patient independent train-validation set from the publicly
available ICBHI 2017 lung sound dataset. Employing the proposed framework,
weighted accuracy scores of 99.20% for ternary chronic classification and
99.05% for six-class pathological classification are achieved, which outperform
well-known and much larger VGG16 in terms of accuracy by 0.52% and 1.77%
respectively. The proposed CNN model also outperforms other contemporary
lightweight models while being computationally comparable.
