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Computer-assisted lip diagnosis on traditional Chinese medicine using multi-class support vector machines

Overview of attention for article published in BMC Complementary and Alternative Medicine, August 2012
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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 (92nd percentile)

Mentioned by

blogs
1 blog
twitter
3 tweeters
patent
2 patents

Citations

dimensions_citation
43 Dimensions

Readers on

mendeley
46 Mendeley
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Title
Computer-assisted lip diagnosis on traditional Chinese medicine using multi-class support vector machines
Published in
BMC Complementary and Alternative Medicine, August 2012
DOI 10.1186/1472-6882-12-127
Pubmed ID
Authors

FuFeng Li, Changbo Zhao, Zheng Xia, Yiqin Wang, Xiaobo Zhou, Guo-Zheng Li

Abstract

In Traditional Chinese Medicine (TCM), the lip diagnosis is an important diagnostic method which has a long history and is applied widely. The lip color of a person is considered as a symptom to reflect the physical conditions of organs in the body. However, the traditional diagnostic approach is mainly based on observation by doctor's nude eyes, which is non-quantitative and subjective. The non-quantitative approach largely depends on the doctor's experience and influences accurate the diagnosis and treatment in TCM. Developing new quantification methods to identify the exact syndrome based on the lip diagnosis of TCM becomes urgent and important. In this paper, we design a computer-assisted classification model to provide an automatic and quantitative approach for the diagnosis of TCM based on the lip images.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 46 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 24%
Researcher 6 13%
Student > Bachelor 6 13%
Student > Master 4 9%
Student > Doctoral Student 3 7%
Other 8 17%
Unknown 8 17%
Readers by discipline Count As %
Medicine and Dentistry 12 26%
Computer Science 9 20%
Agricultural and Biological Sciences 4 9%
Engineering 3 7%
Biochemistry, Genetics and Molecular Biology 1 2%
Other 6 13%
Unknown 11 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 12 November 2021.
All research outputs
#1,758,363
of 21,131,454 outputs
Outputs from BMC Complementary and Alternative Medicine
#302
of 3,013 outputs
Outputs of similar age
#11,113
of 146,970 outputs
Outputs of similar age from BMC Complementary and Alternative Medicine
#1
of 1 outputs
Altmetric has tracked 21,131,454 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,013 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.5. This one has done well, scoring higher than 89% 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 146,970 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 1 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