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Microscopic nuclei classification, segmentation, and detection with improved deep convolutional neural networks (DCNN)

Overview of attention for article published in Diagnostic Pathology, April 2022
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  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
1 X user

Readers on

mendeley
32 Mendeley
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Title
Microscopic nuclei classification, segmentation, and detection with improved deep convolutional neural networks (DCNN)
Published in
Diagnostic Pathology, April 2022
DOI 10.1186/s13000-022-01189-5
Pubmed ID
Authors

Zahangir Alom, Vijayan K. Asari, Anil Parwani, Tarek M. Taha

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 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 4 13%
Student > Master 2 6%
Student > Doctoral Student 1 3%
Lecturer > Senior Lecturer 1 3%
Student > Ph. D. Student 1 3%
Other 1 3%
Unknown 22 69%
Readers by discipline Count As %
Computer Science 5 16%
Unspecified 4 13%
Engineering 2 6%
Medicine and Dentistry 1 3%
Unknown 20 63%
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 19 April 2022.
All research outputs
#19,012,063
of 23,570,677 outputs
Outputs from Diagnostic Pathology
#773
of 1,158 outputs
Outputs of similar age
#316,012
of 442,804 outputs
Outputs of similar age from Diagnostic Pathology
#6
of 13 outputs
Altmetric has tracked 23,570,677 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,158 research outputs from this source. They receive a mean Attention Score of 2.9. This one is in the 16th percentile – i.e., 16% 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 442,804 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.