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COVID-19 detection from Xray and CT scans using transfer learning |
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| Author: | Berrimi, Mohamed1; Hamdi, Skander1; Yahia Cherif, Raoudha1; |
| Organizations: |
1Dep. of Computer Science University of Ferhat Abbes Setif I Setif, Algeria 2Dep. of Computer Science and Engineering University of Oulu Oulu, Finland |
| Format: | article |
| Version: | accepted version |
| Access: | open |
| Online Access: | PDF Full Text (PDF, 3 MB) |
| Persistent link: | http://urn.fi/urn:nbn:fi-fe2021100449271 |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers,
2021
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| Publish Date: | 2021-10-04 |
| Description: |
AbstractSince the novel coronavirus SARS-CoV-2 outbreak, intensive research has been conducted to find suitable tools for diagnosis and identifying infected people in order to take appropriate action. Chest imaging plays a significant role in this phase where CT and Xrays scans have proven to be effective in detecting COVID-19 within the lungs. In this research, we propose deep learning models using Transfer learning to detect COVID-19. Both X-ray and CT scans were considered to evaluate the proposed methods. see all
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| ISBN: | 978-1-6654-4948-9 |
| ISBN Print: | 978-1-6654-4949-6 |
| Pages: | 1 - 6 |
| Article number: | 9430229 |
| DOI: | 10.1109/WIDSTAIF52235.2021.9430229 |
| OADOI: | https://oadoi.org/10.1109/WIDSTAIF52235.2021.9430229 |
| Host publication: |
2021 International Conference of Women in Data Science at Taif University (WiDSTaif 2021) : Taif University, Taif, Saudi Arabia, March 30-31, 2021 |
| Conference: |
International Conference of Women in Data Science at Taif University (WiDSTaif) |
| Type of Publication: |
A4 Article in conference proceedings |
| Field of Science: |
113 Computer and information sciences |
| Subjects: | |
| Copyright information: |
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