Three ResNet Deep Learning Architectures Applied in Pulmonary Pathologies Classification

During the diagnosis of pulmonary diseases, the doctor listens to the lung sounds on the patient’s chest using a traditional stethoscope for the earlier detection of abnormal respiratory sounds. Currently, due to developments in the digital technology domain, an electronic stethoscope can instead be used to acquire respiratory sounds and save these as data for further processing and analysing. Many algorithms for automatic classification have been implemented to distinguish between several lung diseases. In this study, we implemented a deep residual network (ResNet) model with three different architectures based on different numbers of layers (ResNet-50/101/152) for the classification of pulmonary pathologies. To evaluate this method, we used it for the analysis of gammatonegrams, which transform lung sounds from onedimensional to two-dimensional representations. The image outputs obtained with the gammatonegram are fed as inputs to the Three-ResNet architecture. The ICBHI database was used to classify three types of pulmonary conditions, namely, healthy, chronic obstructive pulmonary disease (COPD) and pneumonia conditions. The results showed that, ResNet-50, ResNet-101 and ResNet-152 presented accuracies of 90.37%, 89.79% and 67.57%, respectively. Therefore, our results demonstrate that the residual networks can achieve significant accuracy for the classification of these three types of pulmonary conditions.