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Hyperspectral hybrid method classification for detecting altered mucosa of the human larynx

Overview of attention for article published in International Journal of Health Geographics, January 2012
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1 Facebook page

Citations

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27 Dimensions

Readers on

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48 Mendeley
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Title
Hyperspectral hybrid method classification for detecting altered mucosa of the human larynx
Published in
International Journal of Health Geographics, January 2012
DOI 10.1186/1476-072x-11-21
Pubmed ID
Authors

Ron Martin, Boris Thies, Andreas OH Gerstner

Abstract

In the field of earth observation, hyperspectral detector systems allow precise target detections of surface components from remote sensing platforms. This enables specific land covers to be identified without the need to physically travel to the areas examined. In the medical field, efforts are underway to develop optical technologies that detect altering tissue surfaces without the necessity to perform an excisional biopsy. With the establishment of expedient classification procedures, hyperspectral imaging may provide a non-invasive diagnostic method that allows determination of pathological tissue with high reliability. In this study, we examined the performance of a hyperspectral hybrid method classification for the automatic detection of altered mucosa of the human larynx.

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 2 4%
United Kingdom 1 2%
Spain 1 2%
Turkey 1 2%
Unknown 43 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 19%
Student > Master 7 15%
Student > Doctoral Student 6 13%
Student > Ph. D. Student 6 13%
Professor > Associate Professor 4 8%
Other 9 19%
Unknown 7 15%
Readers by discipline Count As %
Engineering 15 31%
Medicine and Dentistry 9 19%
Agricultural and Biological Sciences 5 10%
Psychology 2 4%
Computer Science 1 2%
Other 5 10%
Unknown 11 23%

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 21 June 2012.
All research outputs
#11,160,852
of 12,545,316 outputs
Outputs from International Journal of Health Geographics
#400
of 475 outputs
Outputs of similar age
#106,636
of 124,855 outputs
Outputs of similar age from International Journal of Health Geographics
#7
of 8 outputs
Altmetric has tracked 12,545,316 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 475 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. 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 124,855 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 8 others from the same source and published within six weeks on either side of this one.