Recognition of Different Pulmonary Diseases from Lung Sounds Using Convolutional Neural Networks

Pulmonary diseases are a group of conditions that affect the lungs. They can cause a variety of symptoms, including shortness of breath, coughing, wheezing, and chest pain. Some common pulmonary diseases include asthma, COPD, pneumonia, bronchiectasis. In addition, chronic obstructive pulmonary disease (COPD) is expected to be the third leading cause of death by 2030. This study explores the application of Convolution Neural Networks (CNN s) in the automated recognition of pulmonary diseases based on lung sounds. The research focuses on leveraging deep learning techniques to analyze audio data collected from patients, aiming to accurately identify specific pulmonary conditions. By employing CNN s, the study demonstrates the potential of machine learning algorithms in enhancing the efficiency and accuracy of pulmonary disease diagnosis, leading to early detection and timely medical interventions. Index terms: Deep Learning, Convolution neural networks (CNN s), Lung Sounds, Long short term memory(LSTM).