Virtual Healthcare Center for COVID-19 Patient Detection Based on Artificial Intelligence Approaches
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 cont...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Canadian Journal of Infectious Diseases and Medical Microbiology |
| Online Access: | http://dx.doi.org/10.1155/2022/6786203 |
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| author | Seifeddine Messaoud Soulef Bouaafia Amna Maraoui Lazhar Khriji Ahmed Chiheb Ammari Mohsen Machhout |
| author_facet | Seifeddine Messaoud Soulef Bouaafia Amna Maraoui Lazhar Khriji Ahmed Chiheb Ammari Mohsen Machhout |
| author_sort | Seifeddine Messaoud |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c47bcd83b0384311a1b3b3c60cb57bf2 |
| institution | Kabale University |
| issn | 1918-1493 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Canadian Journal of Infectious Diseases and Medical Microbiology |
| spelling | doaj-art-c47bcd83b0384311a1b3b3c60cb57bf22025-02-03T05:59:10ZengWileyCanadian Journal of Infectious Diseases and Medical Microbiology1918-14932022-01-01202210.1155/2022/6786203Virtual Healthcare Center for COVID-19 Patient Detection Based on Artificial Intelligence ApproachesSeifeddine Messaoud0Soulef Bouaafia1Amna Maraoui2Lazhar Khriji3Ahmed Chiheb Ammari4Mohsen Machhout5Laboratory of Electronics and MicroelectronicsLaboratory of Electronics and MicroelectronicsLaboratory of Electronics and MicroelectronicsDepartment of Electrical and Computer EngineeringDepartment of Electrical and Computer EngineeringLaboratory of Electronics and MicroelectronicsAt 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.http://dx.doi.org/10.1155/2022/6786203 |
| spellingShingle | Seifeddine Messaoud Soulef Bouaafia Amna Maraoui Lazhar Khriji Ahmed Chiheb Ammari Mohsen Machhout Virtual Healthcare Center for COVID-19 Patient Detection Based on Artificial Intelligence Approaches Canadian Journal of Infectious Diseases and Medical Microbiology |
| title | Virtual Healthcare Center for COVID-19 Patient Detection Based on Artificial Intelligence Approaches |
| title_full | Virtual Healthcare Center for COVID-19 Patient Detection Based on Artificial Intelligence Approaches |
| title_fullStr | Virtual Healthcare Center for COVID-19 Patient Detection Based on Artificial Intelligence Approaches |
| title_full_unstemmed | Virtual Healthcare Center for COVID-19 Patient Detection Based on Artificial Intelligence Approaches |
| title_short | Virtual Healthcare Center for COVID-19 Patient Detection Based on Artificial Intelligence Approaches |
| title_sort | virtual healthcare center for covid 19 patient detection based on artificial intelligence approaches |
| url | http://dx.doi.org/10.1155/2022/6786203 |
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