Virtual Healthcare Center for COVID-19 Patient Detection Based on Artificial intelligence Approaches

Seifeddine Messaoud, Soulef Bouaafia, Amna Maraoui, Lazhar Kheriji, Ahmed Chiheb Ammari, Mohsen Machhout

Research output: Contribution to journalArticlepeer-review

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. %is 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. %e 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. %en, 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. %e numerical results prove the efficiency of the proposed scheme by achieving an accuracy value over 90% compared with the other schemes.
Original languageEnglish
Article number6786203
JournalCanadian Journal of Infectious Diseases and Medical Microbiology
Volume2022
Publication statusPublished - 2022

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