Classification of normal and abnormal lung sounds using neural network and support vector machines
2013 21st Iranian Conference on Electrical Engineering (ICEE), 2013•ieeexplore.ieee.org
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.
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.
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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