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Pulmonary lesion subtypes recognition of COVID-19 from radiomics data with three-dimensional texture characterization in computed tomography images

Overview of attention for article published in BioMedical Engineering OnLine, December 2021
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Mentioned by

twitter
1 tweeter

Readers on

mendeley
15 Mendeley
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Title
Pulmonary lesion subtypes recognition of COVID-19 from radiomics data with three-dimensional texture characterization in computed tomography images
Published in
BioMedical Engineering OnLine, December 2021
DOI 10.1186/s12938-021-00961-w
Pubmed ID
Authors

Wei Li, Yangyong Cao, Kun Yu, Yibo Cai, Feng Huang, Minglei Yang, Weidong Xie

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 15 100%

Demographic breakdown

Readers by professional status Count As %
Professor 2 13%
Student > Bachelor 1 7%
Professor > Associate Professor 1 7%
Researcher 1 7%
Lecturer 1 7%
Other 0 0%
Unknown 9 60%
Readers by discipline Count As %
Economics, Econometrics and Finance 2 13%
Biochemistry, Genetics and Molecular Biology 1 7%
Unspecified 1 7%
Computer Science 1 7%
Business, Management and Accounting 1 7%
Other 0 0%
Unknown 9 60%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 11 December 2021.
All research outputs
#18,995,993
of 21,353,399 outputs
Outputs from BioMedical Engineering OnLine
#655
of 786 outputs
Outputs of similar age
#380,615
of 462,509 outputs
Outputs of similar age from BioMedical Engineering OnLine
#40
of 57 outputs
Altmetric has tracked 21,353,399 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 786 research outputs from this source. They receive a mean Attention Score of 4.4. This one is in the 1st percentile – i.e., 1% 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 462,509 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 57 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.