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A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning

Overview of attention for article published in Circuits, Systems, and Signal Processing, September 2019
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Mentioned by

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1 X user

Citations

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363 Dimensions

Readers on

mendeley
364 Mendeley
Title
A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning
Published in
Circuits, Systems, and Signal Processing, September 2019
DOI 10.1007/s00034-019-01246-3
Authors

Arshia Rehman, Saeeda Naz, Muhammad Imran Razzak, Faiza Akram, Muhammad Imran

X Demographics

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

Geographical breakdown

Country Count As %
Unknown 364 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 37 10%
Student > Master 31 9%
Researcher 23 6%
Student > Bachelor 20 5%
Lecturer 13 4%
Other 47 13%
Unknown 193 53%
Readers by discipline Count As %
Computer Science 99 27%
Engineering 37 10%
Unspecified 7 2%
Medicine and Dentistry 3 <1%
Agricultural and Biological Sciences 2 <1%
Other 13 4%
Unknown 203 56%
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 16 September 2019.
All research outputs
#15,581,198
of 23,163,378 outputs
Outputs from Circuits, Systems, and Signal Processing
#119
of 596 outputs
Outputs of similar age
#210,457
of 340,817 outputs
Outputs of similar age from Circuits, Systems, and Signal Processing
#6
of 7 outputs
Altmetric has tracked 23,163,378 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 596 research outputs from this source. They receive a mean Attention Score of 1.3. This one has gotten more attention than average, scoring higher than 52% 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 340,817 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one.