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Virtual Healthcare Center for COVID-19 Patient Detection Based on Artificial Intelligence Approaches.
Messaoud, Seifeddine; Bouaafia, Soulef; Maraoui, Amna; Khriji, Lazhar; Ammari, Ahmed Chiheb; Machhout, Mohsen.
Afiliación
  • Messaoud S; Laboratory of Electronics and Microelectronics, Faculty of Sciences, University of Monastir, Monastir, Tunisia.
  • Bouaafia S; Laboratory of Electronics and Microelectronics, Faculty of Sciences, University of Monastir, Monastir, Tunisia.
  • Maraoui A; Laboratory of Electronics and Microelectronics, Faculty of Sciences, University of Monastir, Monastir, Tunisia.
  • Khriji L; Department of Electrical and Computer Engineering, College of Engineering, Sultan Qaboos University, Muscat, Oman.
  • Ammari AC; Department of Electrical and Computer Engineering, College of Engineering, Sultan Qaboos University, Muscat, Oman.
  • Machhout M; Laboratory of Electronics and Microelectronics, Faculty of Sciences, University of Monastir, Monastir, Tunisia.
Can J Infect Dis Med Microbiol ; 2022: 6786203, 2022.
Article en En | MEDLINE | ID: mdl-35069953
At the end of 2019, the infectious coronavirus disease (COVID-19) was reported for the first time in Wuhan, and, since then, it has become a public health issue in China and even worldwide. This pandemic has devastating effects on societies and economies around the world, and poor countries and continents are likely to face particularly serious and long-lasting damage, which could lead to large epidemic outbreaks because of the lack of financial and health resources. The increasing number of COVID-19 tests gives more information about the epidemic spread, and this can help contain the spread to avoid more infection. As COVID-19 keeps spreading, medical products, especially those needed to perform blood tests, will become scarce as a result of the high demand and insufficient supply and logistical means. However, technological tests based on deep learning techniques and medical images could be useful in fighting this pandemic. In this perspective, we propose a COVID-19 disease diagnosis (CDD) tool that implements a deep learning technique to provide automatic symptoms checking and COVID-19 detection. Our CDD scheme implements two main steps. First, the patient's symptoms are checked, and the infection probability is predicted. Then, based on the infection probability, the patient's lungs will be diagnosed by an automatic analysis of X-ray or computerized tomography (CT) images, and the presence of the infection will be accordingly confirmed or not. The numerical results prove the efficiency of the proposed scheme by achieving an accuracy value over 90% compared with the other schemes.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Can J Infect Dis Med Microbiol Año: 2022 Tipo del documento: Article País de afiliación: Túnez Pais de publicación: Egipto
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies Idioma: En Revista: Can J Infect Dis Med Microbiol Año: 2022 Tipo del documento: Article País de afiliación: Túnez Pais de publicación: Egipto