International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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A Novel Approach to COVID-19 Diagnosis Based on Mel Spectrogram Features and Artificial Intelligence Techniques

  • Alfaidi, Aseel (Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah) ;
  • Alshahrani, Abdullah (Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah) ;
  • Aljohani, Maha (Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah)
  • Received : 2022.09.05
  • Published : 2022.09.30
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Abstract

COVID-19 has remained one of the most serious health crises in recent history, resulting in the tragic loss of lives and significant economic impacts on the entire world. The difficulty of controlling COVID-19 poses a threat to the global health sector. Considering that Artificial Intelligence (AI) has contributed to improving research methods and solving problems facing diverse fields of study, AI algorithms have also proven effective in disease detection and early diagnosis. Specifically, acoustic features offer a promising prospect for the early detection of respiratory diseases. Motivated by these observations, this study conceptualized a speech-based diagnostic model to aid in COVID-19 diagnosis. The proposed methodology uses speech signals from confirmed positive and negative cases of COVID-19 to extract features through the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images. This is used in addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 of varying ages and speaking different languages, as demonstrated in the simulations. The proposed methodology depends on deep features, followed by the dimension reduction technique for features to detect COVID-19. As a result, it produces better and more consistent performance than handcrafted features used in previous studies.

Keywords

Acknowledgement

The researchers would like to express their gratitude and appreciation to the Department of Computer Science and Technology at the University of Cambridge for making the dataset available to them in order for them to complete their research.

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