| Title |
Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection
|
|---|---|
| Published in |
BioMedical Engineering OnLine, November 2020
|
| DOI | 10.1186/s12938-020-00831-x |
| Pubmed ID | |
| Authors |
Lal Hussain, Tony Nguyen, Haifang Li, Adeel A. Abbasi, Kashif J. Lone, Zirun Zhao, Mahnoor Zaib, Anne Chen, Tim Q. Duong |
X Demographics
The data shown below were collected from the profiles of 10 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 | 20% |
| India | 2 | 20% |
| Germany | 1 | 10% |
| Netherlands | 1 | 10% |
| South Africa | 1 | 10% |
| Unknown | 3 | 30% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 9 | 90% |
| Practitioners (doctors, other healthcare professionals) | 1 | 10% |
Mendeley readers
The data shown below were compiled from readership statistics for 178 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 178 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 23 | 13% |
| Student > Master | 21 | 12% |
| Student > Ph. D. Student | 14 | 8% |
| Student > Postgraduate | 10 | 6% |
| Researcher | 9 | 5% |
| Other | 26 | 15% |
| Unknown | 75 | 42% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 40 | 22% |
| Engineering | 17 | 10% |
| Medicine and Dentistry | 13 | 7% |
| Nursing and Health Professions | 6 | 3% |
| Biochemistry, Genetics and Molecular Biology | 3 | 2% |
| Other | 18 | 10% |
| Unknown | 81 | 46% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 April 2021.
All research outputs
#13,455,803
of 23,885,338 outputs
Outputs from BioMedical Engineering OnLine
#331
of 846 outputs
Outputs of similar age
#226,368
of 513,641 outputs
Outputs of similar age from BioMedical Engineering OnLine
#3
of 9 outputs
Altmetric has tracked 23,885,338 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 846 research outputs from this source. They receive a mean Attention Score of 4.7. 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 513,641 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 55% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.