Diagnosis of Respiratory Sounds using Deep Neural Networks

Abstract

Abstract: Respiratory diseases are on the rise around the world and the impact of COVID-19 has highlighted the need for early and better diagnosis of respiratory diseases. The growing air pollution from vehicles, wildfires, coal-burning power plants and other natural and non-natural causes, particularly in the developing world is leading to more deaths due to respiratory problems leaving many in need of diagnosis and treatment. The use of machine learning for preliminary analysis and diagnosis of diseases is evolving rapidly, particularly in the area of analysis of medical images to help sort through and analyze hundreds of X-ray, CT-Scan or MRI images to highlight the area affected by a potential medical condition and suggest a possible diagnosis. This has allowed timely detection of potentially life-threatening diseases, reduced diagnosis time, improved efficiency and better coverage. These medical aids are also being integrated into the medical scanning equipment and related software to further improve the diagnostic process. This paper attempts to expand the use of deep learning as a method of assisting in the early diagnosis of respiratory diseases using audio recordings. The paper describes an approach for analyzing lung sounds captured using an electronic stethoscope from different parts of a patient’s chest wall. The paper describes the processes used to extract features from the sound signals, dataset preparation, neural network architectures evaluated and the prediction results.