Classification of Lung Sounds and Disease Prediction using Dense CNN Network
Suyash Lakhani1, Ridhi Jhamb2
1Suyash Lakhani*, Student, Department of Computer Science Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
2Ridhi Jhamb, Student, Department of Computer Science Engineering, Vellore Institute of Technology, Vellore (Tamil Nadu), India.
Manuscript received on October 12, 2021. | Revised Manuscript received on October 16, 2021. | Manuscript published on October 30, 2021. | PP: 195-198 | Volume-11 Issue-1, October 2021. | Retrieval Number: 100.1/ijeat.A32071011121 | DOI: 10.35940/ijeat.A3207.1011121
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Respiratory sounds, Classification, Dense Network, Audio files, Respiratory cycles
Scope of the Article: Classification