Classification of Lung Sounds and Disease Prediction using Dense CNN Network

Respiratory illnesses are a main source of death in the world and exact lung sound identification is very significant for conclusion assessment sickness. Be that as it may, this method vulnerable to doctors instrument limitations. As result, automated investigation analysis respiratory sounds has been field great research exploration during last decades. The classification potential distinguish anomalies diseases beginning phases dysfunction hence improve accuracy decision making. In paper, we explore publically available database deploy three different convolutional neural networks (CNN) combine them form dense network diagnose disorders. results demonstrate classifies accurately diagnoses corresponding disorders associated with them.