Title |
Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging
|
---|---|
Published in |
Frontiers in Medicine, July 2020
|
DOI | 10.3389/fmed.2020.00427 |
Pubmed ID | |
Authors |
Seung Hoon Yoo, Hui Geng, Tin Lok Chiu, Siu Ki Yu, Dae Chul Cho, Jin Heo, Min Sung Choi, Il Hyun Choi, Cong Cung Van, Nguen Viet Nhung, Byung Jun Min, Ho Lee |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
Netherlands | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 288 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 288 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Bachelor | 36 | 13% |
Student > Master | 34 | 12% |
Student > Ph. D. Student | 25 | 9% |
Researcher | 16 | 6% |
Lecturer | 15 | 5% |
Other | 46 | 16% |
Unknown | 116 | 40% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 67 | 23% |
Engineering | 32 | 11% |
Medicine and Dentistry | 21 | 7% |
Nursing and Health Professions | 5 | 2% |
Biochemistry, Genetics and Molecular Biology | 4 | 1% |
Other | 29 | 10% |
Unknown | 130 | 45% |
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 09 August 2020.
All research outputs
#15,330,390
of 23,577,654 outputs
Outputs from Frontiers in Medicine
#2,964
of 6,085 outputs
Outputs of similar age
#237,342
of 398,345 outputs
Outputs of similar age from Frontiers in Medicine
#109
of 190 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,085 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.6. This one is in the 46th percentile – i.e., 46% of its peers scored the same or lower than it.
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 398,345 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 190 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.