A comparison of backpropagation and LVQ: A case study of lung sound recognition

Abstract

One way to evaluate the state of the lungs is by listening to breath sounds using stethoscope. This technique is known as auscultation. This technique is fairly simple and inexpensive, but it has some disadvantage. They are the results of subjective analysis, human hearing is less sensitive to low frequency, environmental noise and pattern of lung sounds that almost similar. Because of these factors, misdiagnosis can occur if procedure of auscultation is not done properly. In this research, will be made a model of lung sound recognition with neural network approach. Artificial neural network method used is Backpropagation (BP) and learning Vector Quantization (LVQ). Comparison of these two methods performed to determine and recommend algorithms which provide better recognition accuracy of speech recognition in the case of lung sounds. In addition to the above two methods, the method of Mel Frequency Cepstrum Coefficient (MFCC) is also used as method of feature extraction. The results show the accuracy of using Backpropagation is 93.17%, while the value of using the LVQ is 86.88%. It can be concluded that the introduction of lung sounds using Backpropagation method gives better performance compared to the LVQ method for speech recognition cases of lung sounds.