基于面部表情的可解释性抑郁症检测使用LSTM对注意力中间特征融合与标签光滑
Yanisa Mahayossanunt1, Natawut Nupairoj1,2, Solaphat Hemrungrojn2,3,4
1Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Phayathai Rd, Pathumwan, Bangkok 10330, Thailand.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
这项研究开发了一种机器学习模型,用于早期检测主要抑郁症 (MDD),使用视频中的面部特征. 该模型实现了高准确度,提供了一种透明的方法来识别抑郁症指标.
科学领域:
- 计算机科学 计算机科学
- 精神病学是一个精神病学.
- 医疗成像医学成像
背景情况:
- 大型抑郁症 (MDD) 构成了全球重大健康挑战.
- 早期诊断和干预对于有效管理MDD至关重要.
- 当前的诊断方法可能是耗时和主观的.
研究的目的:
- 使用机器学习开发一种快速的,用于重大抑郁障碍 (MDD) 的预诊断方法.
- 通过分析采访视频中的关键面部特征来检测抑郁症.
- 为抑郁症检测提供可解释的AI模型.
主要方法:
- 使用了具有注意力机制的长短期记忆 (LSTM) 网络.
- 采用中间融合方法来结合提取的面部特征 (例如,目光,动作单位强度).
- 实施标签光滑以提高模型性能,并集成梯度以提高模型可解释性.
主要成果:
- 该模型实现了高性能指标:F1得分为88.89%,回忆率为87.03%,准确率为91.67%,精度为91.40%.
- 成功确定了抑郁症的关键面部指标,包括头转,缺乏特定的目光,眼睛运动缓慢,以及负面面部表情.
- 证明模型能够捕捉与抑郁症理论一致的特征,例如缺乏集中力和社会无兴趣.
结论:
- 拟议的机器学习模型提供了一种有前途,快速和可解释的方法,用于预诊断主要抑郁症 (MDD).
- 视频采访中的面部特征分析可以作为抑郁症检测的宝贵工具.
- 该模型的可解释性通过突出特定患者指标来提高临床效用.
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