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Nonlineer Sınıflandırıcıların Empirical Wavelet Transform Tabanlı Öznitelikler Kullanılarak Solunum Hastalıkları Teşhisi Üzerine Etkisi

  • October 2019
  • Conference: 3. Uluslararası Bilimsel Çalışmalar Kongresi (UBCAK'2019)
  • At: Antalya, Turkey
Authors:
Ahmet Gökçen at Iskenderun Technical University
Ahmet Gökçen
  • Iskenderun Technical University
Gokhan Altan at Iskenderun Technical University
Gokhan Altan
  • Iskenderun Technical University
Yakup Kutlu at Iskenderun Technical University, Hatay, Turkey
Yakup Kutlu
  • Iskenderun Technical University, Hatay, Turkey
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3. Kongresi
(14 16 Ekim 2019 / Antalya)
(UBCAK)
Tam Metin
Adres: -
Telefon: 0532 643 75 23
Mail Adresi: asos@asosyayinlari.com
Web: www.asosyayinlari.com
https://www.instagram.com/asosyayinevi/
Facebook: https://www.facebook.com/asosyayinevi/
Twitter: https://twitter.com/Asosyayinevi
ISBN: 978-605-7736-31-4
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FIBONACCI POEMS AND FIBONACCI POEM EXAMPLES ...................................................................... 145
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A SEMI-AUTOMATED DISK DIFFUSION ZONE DIAMETERS DETERMINATION METHOD BY USING IMAGE
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emredemir1995@gmail.com
ahmet.gokcen@iste.edu.tr
gokhan.altan@iste.edu.tr
Yakup KUTLU
yakup.kutlu@iste.edu.tr
(Empirical Wavelet Transform -
Anahtar Kelimeler:
SVM, MLP
The Effect of Nonlinear Classifiers on the Diagnosis of Respiratory Diseases Using Empirical Wavelet
Transform based Features
Abstract
In this study, Empirical Wavelet Transform (EWT) was applied to lung sounds from
RespiratoryDatabase@TR consisting of 12-channel lung sounds and 4-channel heart sounds. The EWT
is mainly based on the design of an adaptive wavelet filter banks. The statistical features were calculated
from the transformed filters. Then, the performance of the proposed method was evaluated using Support
179
Vector Machines and Multilayer Artificial Neural Networks which are widely used nonlinear classifiers
in literature. The performances of classification algorithms for diagnosis of chronic obstructive
pulmonary disease were effectively evaluated by identifying abnormal patterns on lung sounds.
Although there are different results according to channels and modes, the proposed method has the
highest classification accuracy rates of 80,77% and 90,00% for artificial neural networks and support
vector machines, respectively.
Keywords: Auscultation, COPD, RespiratoryDatabase@TR, Empirical Wavelet Transform, SVM,
MLP
Empirical Wavelet Transform (EWT) uy
listesindedir (WHO, 2018).
ndez-
. EWT ile ilgili olarak ise Maheshwari ve
Makinesi (LS-
yine dijital fundus
-
180
MATERYAL VE METOT
1.1.
-
1
1.2. - EWT)
-
181
2
3
182
4
5
1.3.
etkileyecektir.
183
1.4.
1.5.
1.6.
Makine
(1)
(2)
184
(3)
-
a en
Tablo 1
Hassasiyet Genel
% 87,5 % 70 % 80,77
% 80 % 100 % 90
uygulanabilir.
Altan, G., Kutlu, Y., Garbi,
(RespiratoryDatabase@ TR): Auscultation Sounds and Chest X-rays. Natural and Engineering Sciences,
2(3), 59-72.
185
Altan, G., Kutlu, Y., & Allahverdi, N. (2019). Deep Learning on Computerized Analysis of Chronic
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Babu, K. A., Ramkumar, B., & Manikandan, M. S. (2019, May). Empirical Wavelet Transform Based
Lung Sound Removal from Phonocardiogram Signal for Heart Sound Segmentation. In ICASSP 2019-
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empirical wavelet transform and correntropy features extracted from fundus images. IEEE journal of
biomedical and health informatics, 21(3), 803-813.
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doi:10.1007/978-1-4757-2440-
https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death
ResearchGate has not been able to resolve any citations for this publication.
Deep Learning on Computerized Analysis of Chronic Obstructive Pulmonary Disease
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Glaucoma is an ocular disorder caused due to increased fluid pressure in the optic nerve. It damages the optic nerve subsequently causes loss of vision. The available scanning methods are Heidelberg Retinal Tomography (HRT), Scanning Laser Polarimetry (SLP) and Optical Coherence Tomography (OCT). These methods are expensive and require experienced clinicians to use them. So, there is a need to diagnose glaucoma accurately with low cost. Hence, in this paper, we have presented a new methodology for an automated diagnosis of glaucoma using digital fundus images based on Empirical Wavelet Transform (EWT). The EWT is used to decompose the image and correntropy features are obtained from decomposed EWT components. These extracted features are ranked based on t value feature selection algorithm. Then, these features are used for the classification of normal and glaucoma images using Least Squares Support Vector Machine (LS-SVM) classifier. The LSSVM is employed for classification with Radial Basis Function (RBF), Morlet wavelet and Mexican-hat wavelet kernels. The classification accuracy of proposed method is 98.33% and 96.67% using three-fold and ten-fold cross validation respectively.
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