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AdaBoost-based multiple SVM-RFE for classification of mammograms in DDSM

Overview of attention for article published in BMC Medical Informatics and Decision Making, November 2009
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1 tweeter

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Title
AdaBoost-based multiple SVM-RFE for classification of mammograms in DDSM
Published in
BMC Medical Informatics and Decision Making, November 2009
DOI 10.1186/1472-6947-9-s1-s1
Pubmed ID
Authors

Sejong Yoon, Saejoon Kim

Abstract

Digital mammography is one of the most promising options to diagnose breast cancer which is the most common cancer in women. However, its effectiveness is enfeebled due to the difficulty in distinguishing actual cancer lesions from benign abnormalities, which results in unnecessary biopsy referrals. To overcome this issue, computer aided diagnosis (CADx) using machine learning techniques have been studied worldwide. Since this is a classification problem and the number of features obtainable from a mammogram image is infinite, a feature selection method that is tailored for use in the CADx systems is needed.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
China 3 4%
Germany 1 1%
India 1 1%
Mexico 1 1%
Portugal 1 1%
Russia 1 1%
Spain 1 1%
Japan 1 1%
United States 1 1%
Other 0 0%
Unknown 69 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 29 36%
Researcher 17 21%
Student > Master 14 18%
Student > Bachelor 5 6%
Student > Doctoral Student 3 4%
Other 8 10%
Unknown 4 5%
Readers by discipline Count As %
Computer Science 50 63%
Engineering 10 13%
Medicine and Dentistry 5 6%
Agricultural and Biological Sciences 4 5%
Economics, Econometrics and Finance 2 3%
Other 3 4%
Unknown 6 8%

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 24 February 2014.
All research outputs
#3,061,340
of 4,507,509 outputs
Outputs from BMC Medical Informatics and Decision Making
#632
of 754 outputs
Outputs of similar age
#74,181
of 110,330 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#25
of 30 outputs
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So far Altmetric has tracked 754 research outputs from this source. They receive a mean Attention Score of 4.0. This one is in the 3rd percentile – i.e., 3% 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 110,330 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.