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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Rana, Khattab | - |
| dc.contributor.author | Islam R., Abdelmaksoud | - |
| dc.contributor.author | Samir, Abdelrazek | - |
| dc.date.accessioned | 2023-04-10T02:38:02Z | - |
| dc.date.available | 2023-04-10T02:38:02Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.uri | https://link.springer.com/article/10.1007/s00354-023-00213-6 | - |
| dc.identifier.uri | https://dlib.phenikaa-uni.edu.vn/handle/PNK/7702 | - |
| dc.description | CC BY | vi |
| dc.description.abstract | Coronavirus Disease 2019 (COVID-19), which is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), surprised the world in December 2019 and has threatened the lives of millions of people. Countries all over the world closed worship places and shops, prevented gatherings, and implemented curfews to stand against the spread of COVID-19. Deep Learning (DL) and Artificial Intelligence (AI) can have a great role in detecting and fighting this disease. Deep learning can be used to detect COVID-19 symptoms and signs from different imaging modalities, such as X-Ray, Computed Tomography (CT), and Ultrasound Images (US). This could help in identifying COVID-19 cases as a first step to curing them. In this paper, we reviewed the research studies conducted from January 2020 to September 2022 about deep learning models that were used in COVID-19 detection. | vi |
| dc.language.iso | en | vi |
| dc.publisher | Springer | vi |
| dc.subject | Deep Learning | vi |
| dc.subject | Artificial Intelligence | vi |
| dc.subject | Computed Tomography | vi |
| dc.title | Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey | vi |
| dc.type | Book | vi |
| Appears in Collections | OER - Công nghệ thông tin | |
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