Classification of Respiratory Diseases Using Respiratory Sound Analysis

Author(s):  
R. K. Sawant ◽  
A. A. Ghatol
Author(s):  
Suyash Lakhani ◽  
◽  
Ridhi Jhamb ◽  

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


2018 ◽  
Vol 12 (4) ◽  
pp. 2819-2834 ◽  
Author(s):  
Ferhat Kurtulmuş ◽  
Sencer Öztüfekçi ◽  
İsmail Kavdır

PLoS ONE ◽  
2017 ◽  
Vol 12 (5) ◽  
pp. e0177926 ◽  
Author(s):  
Renard Xaviero Adhi Pramono ◽  
Stuart Bowyer ◽  
Esther Rodriguez-Villegas

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