Publication

Abstract : Deep learning algorithms have been incorporated into conventional auscultation procedures, advancing the area of respiratory sound analysis. Convolutional Neural Network and Gated Recurrent Unit deep learning models were used in a study to increase the precision and effectiveness of respiratory sound categorization (GRU). The work involves developing the models using a sizable dataset of auscultation sounds and assessing how well they classified the sounds into six categories-healthy, bronchiectasis, bronchiolitis, COPD, pneumonia, and URTI-by using auscultation sounds as input. The outcomes indicated that the accuracy of the CNN model was 95%, while the accuracy of the GRU model was 93%. This effort has significantly aided in creating a reliable and effective respiratory sound categorization system.