Lung Sound

Lung sound analysis is a rapidly evolving field focused on using audio recordings to detect and classify respiratory diseases, often in conjunction with heart sound analysis. Current research emphasizes developing accurate and efficient machine learning models, including convolutional neural networks (CNNs) like ResNet and MobileNet, and leveraging techniques like multi-task learning and data augmentation (e.g., RepAugment) to improve diagnostic accuracy, particularly for less prevalent conditions. These advancements hold significant promise for improving the speed and accuracy of respiratory disease diagnosis, potentially leading to earlier interventions and better patient outcomes, particularly in resource-constrained settings.