Classification of normal and abnormal lung sounds using neural network and support vector machines

Samira Abbasi,Yashar Sarbaz,Ataollah Abbasi,Roya Derakhshanfar

doi:10.1109/iraniancee.2013.6599555

Classification of normal and abnormal lung sounds using neural network and support vector machines

Samira Abbasi, Yashar Sarbaz + Show 2 more

https://doi.org/10.1109/iraniancee.2013.6599555

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Publication Date: May 1, 2013

Citations: 37

Affiliation: Sahand University of Technology, University of Tabriz, Islamic Azad University, Science and Research Branch

#Classification Of Lung Sounds #Abnormal Lung Sounds + Show 8 more

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Abstract

This work proposes feature extraction of lung sounds using wavelet coefficients and their classification by neural network and support vector machines. The lung sounds were classified into 6 classes. The results stated the advantages of a support vector machines for the classification of normal and abnormal lung sounds, and indicated that SVMs are a highly successful classifier with accuracy about 93.51-100 for classification of lung sounds.

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