一种混合学习架构用于使用情绪识别检测精神障碍
Joseph Aina1, Oluwatunmise Akinniyi1, Md Mahmudur Rahman2
1Electrical and Computer Engineering Department, School of Engineering, Morgan State University, Baltimore, MD 21251, USA.
IEEE access : practical innovations, open solutions
|July 26, 2024
概括
这项研究引入了一种使用面部表情检测精神障碍的新管道,通过整体模型实现81%的准确性. 该系统有助于早期诊断,并有可能预防精神疾病的严重后果.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 精神病学是一个精神病学.
背景情况:
- 精神疾病是一个全球性的健康问题,需要及时发现和诊断.
- 晚期诊断精神障碍可能会导致严重后果,包括死亡.
- 传统的诊断方法可以通过先进的技术方法来增强.
研究的目的:
- 开发一种用于分析面部表情以检测精神障碍的新型管道.
- 创建一个系统来生成精神障碍数据集,并从面部线索预测疾病.
- 通过人工智能提高精神障碍诊断的准确性和可解释性.
主要方法:
- 利用了AffectNet和2013年面部情绪识别 (FER) 数据集.
- 使用YOLOv8开发了一种混合架构,用于检测与精神障碍相关的视觉线索.
- 实现了一个整体分类器,将卷积神经网络 (CNNs) 和视觉变压器 (ViT) 模型融合在一起.
- 综合梯度加权类激活映射 (Grad-CAM) 和突出地图用于可解释性.
主要成果:
- 在预测精神疾病方面取得了大约81%的整体准确性.
- 成功检测和分类与特定精神障碍相关的视觉线索.
- 提供了对影响诊断预测的特征的可解释的见解.
结论:
- 拟议的人工智能管道为早期和准确的精神障碍检测提供了一个有希望的方法.
- 整体模型提高了诊断的准确性,并为医疗保健专业人员提供了透明度.
- 面部表情分析具有显著的潜力,可以增强传统的心理健康诊断.
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