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Multimodal fusion diagnosis of depression and anxiety based on face video
编号：54 访问权限：仅限参会人 更新：2021-10-30 18:04:16 浏览：752次 张贴报告

报告开始：2021年11月13日 10:05（Asia/Shanghai）

报告时间：5min

所在会场：[Pos] Poster [Pos] Poster Session

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摘要
In order to diagnose depression and anxiety, clinicians will conduct interviews with subjects. If large-scale screening is carried out, this method is too costly and difficult to implement. Because facial expressions play an important role in the diagnosis of clinicians, this provides an opportunity to solve this problem. Therefore, we recorded 303 subjects who answered the self-rated anxiety scale (SAS) and the self-rated depression scale (SDS) Video. Based on Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM), by using either of these two types of videos alone as a binary classification experiment, the accuracy of the diagnosis of depression is 72.53%, and the diagnosis of anxiety is 72.08%. In addition, by fusing the two types of videos to diagnose anxiety, depression, and normal in three categories, the accuracy of the model is 80.22%. Through the comparison of the results, the multimodal fusion diagnosis can not only diagnose the three categories but also has the highest accuracy. This model can be deployed on smartphones, not only for large-scale screening but also to assist doctors in diagnosis.
 
关键词
Depression detection, Anxiety detection, Face video, Long Short-Term Memory Networks,Classification
报告人
Chen Wang
Harbin Engineering University

稿件作者
Chen Wang Harbin Engineering University
Lizhong Liang Sun Yat-Sen University；Marine Biomedical Research Institute of Guangdong
Xiaofeng Liu Harvard Medical School；Suzhou Fanhan Information Technology Co., Ltd
Yao Lu Sun Yat-Sen University；Marine Biomedical Research Institute of Guangdong
Jihong Shen Harbin Engineering University
Hui Luo Southern Marine Science and Engineering Guangdong Laboratory；Marine Biomedical Research Institute of Guangdong
Wanqing Xie Harbin Engineering University；Suzhou Fanhan Information Technology Co., Ltd
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重要日期

  • 会议日期

    11月13日

    2021

    至

    11月14日

    2021

  • 09月30日 2021

    报告提交截止日期

  • 11月14日 2021

    注册截止日期

主办单位
IEEE北京分会
中国生物医学工程学会医学物理分会
中国电子学会生命电子学分会
承办单位
中国科学技术大学
安徽省生物医学工程学会
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