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Automated identification of dementia using medical imaging: a survey from a pattern classification perspective

Overview of attention for article published in Brain Informatics, December 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#16 of 102)
  • High Attention Score compared to outputs of the same age (80th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

Mentioned by

news
1 news outlet

Citations

dimensions_citation
36 Dimensions

Readers on

mendeley
84 Mendeley
Title
Automated identification of dementia using medical imaging: a survey from a pattern classification perspective
Published in
Brain Informatics, December 2015
DOI 10.1007/s40708-015-0027-x
Pubmed ID
Authors

Chuanchuan Zheng, Yong Xia, Yongsheng Pan, Jinhu Chen

Abstract

In this review paper, we summarized the automated dementia identification algorithms in the literature from a pattern classification perspective. Since most of those algorithms consist of both feature extraction and classification, we provide a survey on three categories of feature extraction methods, including the voxel-, vertex- and ROI-based ones, and four categories of classifiers, including the linear discriminant analysis, Bayes classifiers, support vector machines, and artificial neural networks. We also compare the reported performance of many recently published dementia identification algorithms. Our comparison shows that many algorithms can differentiate the Alzheimer's disease (AD) from elderly normal with a largely satisfying accuracy, whereas distinguishing the mild cognitive impairment from AD or elderly normal still remains a major challenge.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Unknown 83 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 15%
Researcher 11 13%
Student > Master 11 13%
Student > Bachelor 6 7%
Lecturer 5 6%
Other 13 15%
Unknown 25 30%
Readers by discipline Count As %
Computer Science 18 21%
Engineering 15 18%
Psychology 4 5%
Agricultural and Biological Sciences 4 5%
Neuroscience 4 5%
Other 10 12%
Unknown 29 35%
Attention Score in Context

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 21 October 2016.
All research outputs
#4,194,102
of 22,896,955 outputs
Outputs from Brain Informatics
#16
of 102 outputs
Outputs of similar age
#70,851
of 389,582 outputs
Outputs of similar age from Brain Informatics
#2
of 8 outputs
Altmetric has tracked 22,896,955 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 102 research outputs from this source. They receive a mean Attention Score of 4.4. This one has done well, scoring higher than 77% 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 389,582 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 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.