Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Convolution Network Based Emotion Analysis for Automatic Detection
  of Mild Cognitive Impairment in the Elderly   A significant number of people are suffering from cognitive impairment all
over the world. Early detection of cognitive impairment is of great importance
to both patients and caregivers. However, existing approaches have their
shortages, such as time consumption and financial expenses involved in clinics
and the neuroimaging stage. It has been found that patients with cognitive
impairment show abnormal emotion patterns. In this paper, we present a novel
deep convolution network-based system to detect the cognitive impairment
through the analysis of the evolution of facial emotions while participants are
watching designed video stimuli. In our proposed system, a novel facial
expression recognition algorithm is developed using layers from MobileNet and
Support Vector Machine (SVM), which showed satisfactory performance in 3
datasets. To verify the proposed system in detecting cognitive impairment, 61
elderly people including patients with cognitive impairment and healthy people
as a control group have been invited to participate in the experiments and a
dataset was built accordingly. With this dataset, the proposed system has
successfully achieved the detection accuracy of 73.3%.

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The categories of this paper are: adap-org
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The categories of this paper are: nucl-th
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The categories of this paper are: stat
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The categories of this paper are: acc-phys
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
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The categories of this paper are: supr-con
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: solv-int
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The categories of this paper are: chao-dyn
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The categories of this paper are: funct-an
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The categories of this paper are: bayes-an
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The categories of this paper are: econ
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The categories of this paper are: cmp-lg