Virtual Healthcare Center for COVID-19 Patient Detection Based on Artificial Intelligence Approaches Article Swipe
Seifeddine Messaoud
,
Soulef Bouaafia
,
Amna Maraoui
,
Lazhar Khriji
,
Ahmed Chiheb Ammari
,
Mohsen Machhout
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/6786203
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/6786203
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.
Related Topics
Concepts
Pandemic
Coronavirus disease 2019 (COVID-19)
Public health
Health care
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Disease
Infectious disease (medical specialty)
Computer science
Artificial intelligence
Outbreak
Deep learning
Medicine
Medical emergency
Virology
Pathology
Economic growth
Economics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2022/6786203
- https://downloads.hindawi.com/journals/cjidmm/2022/6786203.pdf
- OA Status
- gold
- Cited By
- 7
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4206473310
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4206473310Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1155/2022/6786203Digital Object Identifier
- Title
-
Virtual Healthcare Center for COVID-19 Patient Detection Based on Artificial Intelligence ApproachesWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-01-11Full publication date if available
- Authors
-
Seifeddine Messaoud, Soulef Bouaafia, Amna Maraoui, Lazhar Khriji, Ahmed Chiheb Ammari, Mohsen MachhoutList of authors in order
- Landing page
-
https://doi.org/10.1155/2022/6786203Publisher landing page
- PDF URL
-
https://downloads.hindawi.com/journals/cjidmm/2022/6786203.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://downloads.hindawi.com/journals/cjidmm/2022/6786203.pdfDirect OA link when available
- Concepts
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Pandemic, Coronavirus disease 2019 (COVID-19), Public health, Health care, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Disease, Infectious disease (medical specialty), Computer science, Artificial intelligence, Outbreak, Deep learning, Medicine, Medical emergency, Virology, Pathology, Economic growth, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2, 2023: 4, 2022: 1Per-year citation counts (last 5 years)
- References (count)
-
48Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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