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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
  • Good Attention Score compared to outputs of the same age (76th percentile)

Mentioned by

1 news outlet


21 Dimensions

Readers on

73 Mendeley
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

Chuanchuan Zheng, Yong Xia, Yongsheng Pan, Jinhu Chen


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

The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Unknown 72 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 16%
Student > Master 11 15%
Researcher 11 15%
Student > Bachelor 5 7%
Student > Doctoral Student 4 5%
Other 12 16%
Unknown 18 25%
Readers by discipline Count As %
Engineering 16 22%
Computer Science 16 22%
Agricultural and Biological Sciences 4 5%
Nursing and Health Professions 3 4%
Neuroscience 3 4%
Other 8 11%
Unknown 23 32%

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
of 8,547,393 outputs
Outputs from Brain Informatics
of 34 outputs
Outputs of similar age
of 250,863 outputs
Outputs of similar age from Brain Informatics
of 2 outputs
Altmetric has tracked 8,547,393 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 34 research outputs from this source. They receive a mean Attention Score of 3.7. This one scored the same or higher as 30 of them.
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 250,863 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 76% of its contemporaries.
We're also able to compare this research output to 2 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