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Computer-aided detection system for lung cancer in computed tomography scans: Review and future prospects

Overview of attention for article published in BioMedical Engineering OnLine, January 2014
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (80th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

twitter
7 tweeters

Citations

dimensions_citation
97 Dimensions

Readers on

mendeley
163 Mendeley
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Title
Computer-aided detection system for lung cancer in computed tomography scans: Review and future prospects
Published in
BioMedical Engineering OnLine, January 2014
DOI 10.1186/1475-925x-13-41
Pubmed ID
Authors

Macedo Firmino, Antônio H Morais, Roberto M Mendoça, Marcel R Dantas, Helio R Hekis, Ricardo Valentim

Abstract

The goal of this paper is to present a critical review of major Computer-Aided Detection systems (CADe) for lung cancer in order to identify challenges for future research. CADe systems must meet the following requirements: improve the performance of radiologists providing high sensitivity in the diagnosis, a low number of false positives (FP), have high processing speed, present high level of automation, low cost (of implementation, training, support and maintenance), the ability to detect different types and shapes of nodules, and software security assurance.

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters 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 163 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Iran, Islamic Republic of 1 <1%
Korea, Republic of 1 <1%
United States 1 <1%
Brazil 1 <1%
Unknown 159 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 31 19%
Student > Master 31 19%
Researcher 23 14%
Student > Bachelor 17 10%
Student > Postgraduate 9 6%
Other 28 17%
Unknown 24 15%
Readers by discipline Count As %
Computer Science 37 23%
Engineering 35 21%
Medicine and Dentistry 24 15%
Social Sciences 4 2%
Biochemistry, Genetics and Molecular Biology 3 2%
Other 21 13%
Unknown 39 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 23 November 2019.
All research outputs
#3,366,981
of 18,796,975 outputs
Outputs from BioMedical Engineering OnLine
#83
of 738 outputs
Outputs of similar age
#36,630
of 201,042 outputs
Outputs of similar age from BioMedical Engineering OnLine
#1
of 10 outputs
Altmetric has tracked 18,796,975 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 738 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done well, scoring higher than 86% 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 201,042 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 80% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them