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X Demographics
Mendeley readers
Attention Score in Context
| Title |
The Effectiveness of Image Augmentation in Deep Learning Networks for Detecting COVID-19: A Geometric Transformation Perspective
|
|---|---|
| Published in |
Frontiers in Medicine, March 2021
|
| DOI | 10.3389/fmed.2021.629134 |
| Pubmed ID | |
| Authors |
Mohamed Elgendi, Muhammad Umer Nasir, Qunfeng Tang, David Smith, John-Paul Grenier, Catherine Batte, Bradley Spieler, William Donald Leslie, Carlo Menon, Richard Ribbon Fletcher, Newton Howard, Rabab Ward, William Parker, Savvas Nicolaou |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 25% |
| Netherlands | 1 | 25% |
| Canada | 1 | 25% |
| Switzerland | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 50% |
| Scientists | 1 | 25% |
| Practitioners (doctors, other healthcare professionals) | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 151 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 151 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 19 | 13% |
| Student > Bachelor | 18 | 12% |
| Student > Ph. D. Student | 11 | 7% |
| Student > Doctoral Student | 11 | 7% |
| Researcher | 9 | 6% |
| Other | 17 | 11% |
| Unknown | 66 | 44% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 29 | 19% |
| Engineering | 24 | 16% |
| Medicine and Dentistry | 8 | 5% |
| Business, Management and Accounting | 4 | 3% |
| Social Sciences | 4 | 3% |
| Other | 13 | 9% |
| Unknown | 69 | 46% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 October 2021.
All research outputs
#13,736,777
of 23,285,523 outputs
Outputs from Frontiers in Medicine
#2,249
of 5,963 outputs
Outputs of similar age
#205,891
of 419,815 outputs
Outputs of similar age from Frontiers in Medicine
#160
of 338 outputs
Altmetric has tracked 23,285,523 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,963 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.5. This one has gotten more attention than average, scoring higher than 60% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 419,815 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 338 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.