Virtual Healthcare Center for COVID-19 Patient Detection Based on Artificial Intelligence Approaches. (11th January 2022)

• Record Type: Journal Article
• Authors: Messaoud, Seifeddine; Bouaafia, Soulef; Maraoui, Amna; Khriji, Lazhar; Ammari, Ahmed Chiheb; Machhout, Mohsen
• Other Names: Arabi Mariam Academic Editor.
• Is Part Of: Canadian journal of infectious diseases & medical microbiology =. Volume 2022(2022)
• Publication Date: 2022-01-11
• Subjects: Communicable diseases -- Periodicals; Infection -- Periodicals; Communicable diseases; Infection; Communicable Diseases; Communicable Disease Control; Electronic journals; Periodicals; Fulltext; Internet Resources; Periodicals; 616.9
• DOI: 10.1155/2022/6786203
• Languages: English
• ISSNs: 1712-9532

Abstract

Abstract : 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.