Development of a Web App for Asthmatic Wheeze Detection using Convolutional Neural Networks

Abstract

Asthma and Chronic Obstructive Pulmonary Disease (COPD) are critical lung conditions characterized by breathing difficulties. In asthma, airways become constricted, inflamed, and filled with mucus, leading to symptoms such as wheezing, coughing, and shortness of breath. Wheezing serves as a vital diagnostic indicator for these and other respiratory disorders. Early detection and management are crucial to prevent severe complications and improve patient outcomes. This research introduces a web application for asthmatic wheeze detection, employing Convolutional Neural Networks (CNNs) to enable early identification of respiratory disorders in Sri Lanka. Our system captures audio recordings from an electronic stethoscope, processes the data using a CNN model, and detects wheezes with an impressive accuracy of 84%. The application not only identifies wheezing but also provides tailored therapy recommendations and dosage prescriptions based on the detected condition which is collected by a healthcare professional. By leveraging this advanced technology, we aim to revolutionize respiratory health monitoring in Sri Lanka, offering healthcare professionals a reliable tool for timely intervention and enhancing patient care.