| Abstract [eng] |
The COVID-19 pandemic is one of the most disruptive outbreaks of the 21st century considering its impacts on our freedoms and social lifestyle. Several methods have been used to monitor and diagnose this virus, which includes the use of RT-PCR test and chest CT/CXR scans. Recent studies have employed various crowdsourced sound data types such as coughing, breathing, sneezing, etc., for the detection of COVID-19. However, the application of artificial intelligence methods and machine learning algorithms on these sound datasets still suffer some limitations such as the poor performance of the test results due to increase of misclassified data, limited datasets resulting in the overfitting of deep learning methods, the high computational cost of some augmentation models, and varying quality feature-extracted images resulting in poor reliability. We propose a simple yet effective deep learning model, called DeepShufNet, for COVID-19 detection. A data augmentation method based on the color transformation and noise addition was used for generating synthetic image datasets from sound data. The efficiencies of the synthetic dataset were evaluated using two feature extraction approaches, namely Mel spectrogram and GFCC. The performance of the proposed DeepShufNet model was evaluated using a deep breathing COSWARA dataset, which shows improved performance with a lower misclassification rate of the minority class. The proposed model achieved an accuracy, precision, recall, specificity, and f-score of 90.1%, 77.1%, 62.7%, 95.98%, and 69.1%, respectively, for positive COVID-19 detection using the Mel COCOA-2 augmented training datasets. The proposed model showed an improved performance compared to some of the state-of-the-art-methods. |
