Research Article
Healthy and Asthmatic Sounds Classification using Fuzzy Clustering based Cascade Classifier Methods
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 182 - Issue 25 |
| Published: Nov 2018 |
| Authors: Güneş Harman |
10.5120/ijca2018918098
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Güneş Harman . Healthy and Asthmatic Sounds Classification using Fuzzy Clustering based Cascade Classifier Methods. International Journal of Computer Applications. 182, 25 (Nov 2018), 34-40. DOI=10.5120/ijca2018918098
@article{ 10.5120/ijca2018918098,
author = { Güneş Harman },
title = { Healthy and Asthmatic Sounds Classification using Fuzzy Clustering based Cascade Classifier Methods },
journal = { International Journal of Computer Applications },
year = { 2018 },
volume = { 182 },
number = { 25 },
pages = { 34-40 },
doi = { 10.5120/ijca2018918098 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2018
%A Güneş Harman
%T Healthy and Asthmatic Sounds Classification using Fuzzy Clustering based Cascade Classifier Methods%T
%J International Journal of Computer Applications
%V 182
%N 25
%P 34-40
%R 10.5120/ijca2018918098
%I Foundation of Computer Science (FCS), NY, USA
Abstract
In this study, our aim is to presents different cascade classifier models based on Fuzzy C-means clustering methods, to classified normal and asthmatic lung sounds. The lung sounds recorded from both left and right lobe of the lung and divided inspiration and expiration phases. The frequency spectrum is obtained by using Fast Fourier Transform (FFT). After obtaining the spectrums of healthy and asthmatic lung sounds, fuzzy c-mean is applied to cluster the spectrum and then supervised classifier ANN, Naive Bayes and SVM are applied to classified healthy and asthmatic lung sounds.
References
- D. Z. and O. T., “Classification of respiratory sounds by using an artificial neural network,” Int. J. Pattern Recognit. Artif. Intell., vol. 17, pp. 567–580, 2003.
- A. Kandaswamy, C. S. Kumar, R. P. Ramanathan, S. Jayaraman, and N. Malmurugan, “Neural classification of lung sounds using wavelet coefficients,” Comput. Biol. Med., vol. 34, pp. 523–537, 2004.
- S. Abbasi, R. Derakhshanfar, A. Abbasi, and Y. Sarbaz, “Classification of normal and abnormal lung sounds using neural network and support vector machines,” 2013 21st Iran. Conf. Electr. Eng., pp. 1–4, May 2013.
- S. Uysal, H. Uysal, B. Bolat, and T. Yıldırım, “Classification of Normal and Abnormal Lung Sounds Using Wavelet Coefficients,” 2014 IEEE 22nd Signal Process. Commun. Appl. Conf. (SIU 2014), no. Siu, pp. 2138–2141, 2014.
- W. Peizhuang, “Pattern Recognition with Fuzzy Objective Function Algorithms (James C. Bezdek),” SIAM Rev., vol. 25, no. 3, pp. 442–442, 1983.
- B. Karlik, “Machine learning algorithms for chacarterization of EMG signals,” Int. J. Inf. Electron. Eng., vol. 4, pp. 189–194, 2014.
- B. Karlik, “Hepatitis Disease Diagnosis Using Backpropagation and the Naive Bayes Classifiers,” J. Sci. Technol., vol. 1, pp. 49–62, 2011.
- B. Karlik, M. O. Tokhi, and M. Alci, “A Fuzzy Clustering Neural Network Architecture for Multifunction Upper-Limb Prosthesis,” IEEE Trans. Biomed. Eng., vol. 50, no. 11, pp. 1255–1261, 2003.
- B. Karlık, “The effects of fuzzy clustering on the back-propagation algorithm,” in International Conference, 2014.
- B. Karlık and K. Yüksek, “Fuzzy Clustering Neural Networks for Real-Time Odor Recognition System,” J. Autom. Methods Manag. Chem., vol. 2007, p. 38405, 2007
- Karlık Bekir, Güçlü, Güneş, Öz Halil Rıdvan, “Respiratory Sounds Classification Fuzzy C-means , Principal Component Analysis and Artificial Neural Network,” in 1st International symposium on computing in science & engineering Bioengineering congress, 2010.
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