A Novel Local Feature Generation Technique Based Sound Classification Method for Covid-19 Detection using Lung Breathing Sound

Türker TUNCER; Erhan AKBAL; Emrah AYDEMİR; Samir Brahim BELHAOUARI; Sengul DOGAN

doi:https://doi.org/10.36222/ejt.986599

Makale detayları

A Novel Local Feature Generation Technique Based Sound Classification Method for Covid-19 Detection using Lung Breathing Sound

Yazarlar:

Türker TUNCER | Erhan AKBAL | Emrah AYDEMİR | Samir Brahim BELHAOUARI | Sengul DOGAN

Yayıncı:

Hibetullah KILIÇ

Ana Başlıklar:

Veri işleme & bilgisayar bilimi

Dergi:

European Journal of Technique (EJT) 2021 Vol.11 Issue 2, pp.165-174

Belge Tipi:

Makale

Yayın Tarihi:

12/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000425438

eISSN:

2536-5134

pISSN:

2536-5010

DOI:

https://doi.org/10.36222/ejt.986599

Özet:

 

Lung breathing sounds have been used to diagnose many diseases, including Covid-19. Nowadays, Covid-19 has affected daily life worldwide, and it has caused a global pandemic.  Generally, computer vision methods have been presented to classify healthy, pneumonia, and Covid-19. They achieved high classification rates on datasets with a limited number of classes without taking into consideration other lung diseases. Our main hypothesis is to detect Covid-19 automatically among other lung diseases by using lung breathing sounds. Therefore, a dataset of lung breathing sound with ten classes has been collected, and a novel lung sounds classification method has been proposed in this paper. This method presents a novel local feature generation technique, and Substitution Box (S-Box) of the present lightweight encryption method is utilized as a pattern. A novel nonlinear pattern is presented based on S-Box, named Present-SBox-Pat (present S-Box pattern). A new pooling-based transformation (maximum tent pooling (MaTP)) is proposed to generate high, middle, and low levels features. It is considered as a preprocessing method of this work. ReliefF and iterative neighbourhood component analysis (RFINCA) selector is used to select the most discriminative and informative features. Two shallow classifiers are used to obtain results.  The proposed Present-SBox-Pat and MaTP feature generation network and RFINCA feature selector-based method achieved 95.43% classification accuracy using the SVM classifier. These results demonstrated the success of techniques in generating and selecting features that facilitate the task of classifiers.

Makale detayları

A Novel Local Feature Generation Technique Based Sound Classification Method for Covid-19 Detection using Lung Breathing Sound

Yazarlar: Türker TUNCER | Erhan AKBAL | Emrah AYDEMİR | Samir Brahim BELHAOUARI | Sengul DOGAN

Yayıncı: Hibetullah KILIÇ

Ana Başlıklar: Veri işleme & bilgisayar bilimi

Dergi: European Journal of Technique (EJT) 2021 Vol.11 Issue 2, pp.165-174

Belge Tipi: Makale

Yayın Tarihi: 12/2021

Belge Dili: İngilizce

Document ID (Org No.): 2000425438

eISSN: 2536-5134

pISSN: 2536-5010

DOI: https://doi.org/10.36222/ejt.986599

Sayı

Yazarlar:

Türker TUNCER | Erhan AKBAL | Emrah AYDEMİR | Samir Brahim BELHAOUARI | Sengul DOGAN

Yayıncı:

Hibetullah KILIÇ

Ana Başlıklar:

Veri işleme & bilgisayar bilimi

Dergi:

European Journal of Technique (EJT) 2021 Vol.11 Issue 2, pp.165-174

Belge Tipi:

Makale

Yayın Tarihi:

12/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000425438

eISSN:

2536-5134

pISSN:

2536-5010

DOI:

https://doi.org/10.36222/ejt.986599