| dc.description.abstract | Asthma and chronic obstructive pulmonary diseases
(COPD) are two lung conditions that frequently exhibit breathing
problems. If you have asthma, your airways may become more
constricted, enlarged, and mucus-producing. This could block your
airways and result in wheezing, whining, coughing, and shortness
of breath. As a result, wheezing can be a vital diagnostic tool for
determining the presence of many disorders. An individual's
respiratory rate increases when they wheeze, and as a result, their
lungs are more likely to work harder than they normally would, and
it will pose a significant health challenge and can lead to severe
complications if not detected and managed early. In this research,
we present a web application for asthmatic wheeze detection using
Convolutional Neural Networks (CNNs) for the early identification
of respiratory disorders in Sri Lanka. The system leverages a web
application server to receive audio recordings from an electronic
stethoscope and applies a CNN model to analyse the data and detect
wheeze. Additionally, the system provides therapy recommendations
and dosage prescriptions based on the detected respiratory
disorder. The developed model achieves an accuracy of 74.68% in
wheeze detection. This research aims to improve respiratory health
monitoring in Sri Lanka and provide healthcare professionals with
a reliable tool for early intervention | en_US |
