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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Explainable Machine Learning for COVID-19 Pneumonia Classification With Texture-Based Features Extraction in Chest Radiography
|
|---|---|
| Published in |
Frontiers in Digital Health, January 2022
|
| DOI | 10.3389/fdgth.2021.662343 |
| Pubmed ID | |
| Authors |
Luís Vinícius de Moura, Christian Mattjie, Caroline Machado Dartora, Rodrigo C. Barros, Ana Maria Marques da Silva |
X Demographics
The data shown below were collected from the profiles of 8 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 | 2 | 25% |
| Netherlands | 1 | 13% |
| Brazil | 1 | 13% |
| Switzerland | 1 | 13% |
| Unknown | 3 | 38% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 75% |
| Scientists | 1 | 13% |
| Practitioners (doctors, other healthcare professionals) | 1 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 30 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 3 | 10% |
| Lecturer | 2 | 7% |
| Student > Bachelor | 2 | 7% |
| Researcher | 2 | 7% |
| Student > Ph. D. Student | 2 | 7% |
| Other | 1 | 3% |
| Unknown | 18 | 60% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 4 | 13% |
| Medicine and Dentistry | 2 | 7% |
| Physics and Astronomy | 1 | 3% |
| Business, Management and Accounting | 1 | 3% |
| Sports and Recreations | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 20 | 67% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 06 February 2022.
All research outputs
#6,822,691
of 24,901,761 outputs
Outputs from Frontiers in Digital Health
#242
of 757 outputs
Outputs of similar age
#144,255
of 511,741 outputs
Outputs of similar age from Frontiers in Digital Health
#16
of 62 outputs
Altmetric has tracked 24,901,761 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 757 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.6. This one has gotten more attention than average, scoring higher than 67% 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 511,741 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 62 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 74% of its contemporaries.