Different Respiratory Lung Sounds Prediction using Deep Learning

Abstract

The sounds produced by the lungs when breathing might provide important information to physicians. Based on the findings, a deep learning-based approach is recommended for the prediction of breathing-related lung sounds. The Proposed model was trained in lung sounds collected from people suffering from a broad variety of respiratory conditions. The research improves classifying lung sounds, by audio to image spectrogram features is taken and used to train a deep convolutional neural network. The proposed technique accurately predicts many different types of respiratory lung sounds, demonstrating the promise of deep learning in this domain. This research results have important implications for the development of automated diagnostic tools that might help doctors make correct diagnoses of respiratory disorders more quickly and accurately.