A Lightweight CNN Model for Detecting Respiratory Diseases From Lung Auscultation Sounds Using EMD-CWT-Based Hybrid Scalogram

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