Artificial Intelligence Against Virus Changes: A Long Term Detector of COVID-19 using the Clinical Symptoms and Respiratory Sounds. | ||
| Journal of Advanced Engineering Trends | ||
| Volume 42, Issue 2, July 2023, Pages 289-299 PDF (1.21 M) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/jaet.2022.132552.1149 | ||
| Authors | ||
| Kamel Rahouma1; Safwat Mohamed Ramzy2; Mahmoud Aly* 1 | ||
| 1Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia, Egypt | ||
| 2Department of Electrical Engineering, Faculty of Engineering, Sohag University, Sohag, Egypt | ||
| Abstract | ||
| In this research, we introduce our methods for creating a COVID-19 diagnostic tool based on the artificial intelligence to analyze the data of COVID-19 external symptoms such as cough, respiratory sounds, and other clinical symptoms such as fever, muscle pain, cold, sore throat, asthma, etc. to detect the virus without the need of any chemical or clinical tests which are expensive, slow and not available everywhere. Our diagnostic tool can be used publicly in crowded places such as shops, schools, or any human gatherings to detect the patients in their early stages, reduce the virus spread and forward the suspected people to clinical examination. For creating our tool, we used deep learning-based models to analyze and learn from the collected sounds and the clinical features of confirmed COVID-19 cases and other normal cases. By using those models as classifiers they could distinguish the positive cases from the negatives. And we found that using simple binary classifiers trained with small samples of COVID-19 data collected early in 2021 can be trustworthy to detect COVID-19 in the recently collected samples regardless of the changes that occurred to the virus. And by testing the samples collected from 313 cases after several months of training our models, we could achieve an average accuracy of 91% to prove the proficiency of our tool in diagnosing COVID-19 and detecting the virus in the long term after several mutations. | ||
| Keywords | ||
| COVID-19; AI; Cough sounds; Clinical Information; Respiratory sounds | ||
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