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

Authors : Türker TUNCER, Erhan AKBAL, Emrah AYDEMİR, Samir Brahim BELHAOUARI, Sengul DOGAN

Pages : 165-174

Doi:10.36222/ejt.986599

View : 50 | Download : 12

Publication Date : 2021-12-30

Article Type : Research Paper

Abstract :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 insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(present S-Box pattern);. A new pooling-based transformation insert ignore into journalissuearticles values(maximum tent pooling insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(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.
Keywords : Lung breathing sound, present SBox pattern, Covid 19, maximum tent pooling, 2 levelled feature selector

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