基于MRI图像和语音信号的多模式抑郁症检测的联合学习和深度学习框架
Minakshee Patil1, Prachi Mukherji1, Vijay Wadhai2
1Electronics and Telecommunication, MKSSS's Cummins College of Engineering for Women, Pune, Cummins College Rd, Karve Nagar, Pune, Maharashtra 411052, India.
Computational biology and chemistry
|October 18, 2024
概括
这项研究引入了一种新的深度学习方法,使用联合学习 (FL) 通过分析语音和MRI数据来检测青少年的抑郁症. 该方法实现了高精度,为早期心理健康干预提供了有前途的工具.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 青少年精神病学 青少年精神病学
背景情况:
- 青少年抑郁症是一个日益关注的问题,导致严重的教育,社会和危及生命的风险.
- 早期发现和治疗对于管理青少年心理健康至关重要.
- 传统的深度学习模型难以处理大型数据集,需要高效的解决方案.
研究的目的:
- 开发和评估一个联合学习 (FL) 框架,用于检测青少年的抑郁症.
- 整合多模式数据,包括语音信号和磁共振成像 (MRI),以提高检测准确度.
- 建议在FL框架内为抑郁症识别提供一个优化的深度学习模型.
主要方法:
- 联邦学习 (FL) 框架与本地和全球模块被用于抑郁症检测.
- 本地模块使用了一个基于指数式非洲优化的深度卷积神经网络 (ExpAPO-DCNN).
- 语音信号和MRI数据被单独预处理,特征提取,然后使用重叠系数融合.
主要成果:
- ExpAPO-DCNN模型实现了98.00%的高精度.
- 绩效指标包括0.023的损失,0.058的RMSE和0.240.0的MSE.
- 该模型表现出强大的诊断能力,真负率 (TNR) 为97.90%,真阳性率 (TPR) 为96.30%.
结论:
- 拟议的FL框架有效地解决了深度学习中的数据大小挑战,以检测青少年抑郁症.
- 语音和MRI数据与ExpAPO-DCNN模型的整合显示出对准确和早期诊断的重大承诺.
- 这种方法为青少年的心理健康监测提供了一个可扩展和保护隐私的解决方案.
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