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Evaluation of computed tomography images under deep learning in the diagnosis of severe pulmonary infection

Overview of attention for article published in Frontiers in Computational Neuroscience, August 2023
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1 X user

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4 Mendeley
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Title
Evaluation of computed tomography images under deep learning in the diagnosis of severe pulmonary infection
Published in
Frontiers in Computational Neuroscience, August 2023
DOI 10.3389/fncom.2023.1115167
Pubmed ID
Authors

Mao Ming, Na Lu, Wei Qian

X Demographics

X Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Professor 1 25%
Other 1 25%
Unknown 2 50%
Readers by discipline Count As %
Psychology 1 25%
Medicine and Dentistry 1 25%
Unknown 2 50%
Attention Score in Context

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 25 August 2023.
All research outputs
#21,798,824
of 24,323,543 outputs
Outputs from Frontiers in Computational Neuroscience
#1,221
of 1,411 outputs
Outputs of similar age
#194,404
of 237,197 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#14
of 14 outputs
Altmetric has tracked 24,323,543 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 1,411 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. 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 237,197 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 14 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.