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Timeline
X Demographics
Mendeley readers
| Title |
COVID-FACT: A Fully-Automated Capsule Network-Based Framework for Identification of COVID-19 Cases from Chest CT Scans
|
|---|---|
| Published in |
Frontiers in Artificial Intelligence, May 2021
|
| DOI | 10.3389/frai.2021.598932 |
| Pubmed ID | |
| Authors |
Shahin Heidarian, Parnian Afshar, Nastaran Enshaei, Farnoosh Naderkhani, Moezedin Javad Rafiee, Faranak Babaki Fard, Kaveh Samimi, S. Farokh Atashzar, Anastasia Oikonomou, Konstantinos N. Plataniotis, Arash Mohammadi |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | 20% |
| United States | 1 | 20% |
| Canada | 1 | 20% |
| Switzerland | 1 | 20% |
| Unknown | 1 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 60% |
| Scientists | 1 | 20% |
| Practitioners (doctors, other healthcare professionals) | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 62 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 15% |
| Student > Master | 8 | 13% |
| Researcher | 4 | 6% |
| Lecturer | 4 | 6% |
| Student > Doctoral Student | 3 | 5% |
| Other | 10 | 16% |
| Unknown | 24 | 39% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 20 | 32% |
| Engineering | 6 | 10% |
| Medicine and Dentistry | 4 | 6% |
| Nursing and Health Professions | 1 | 2% |
| Agricultural and Biological Sciences | 1 | 2% |
| Other | 4 | 6% |
| Unknown | 26 | 42% |