混合优化使Eff-FDMNet能够在联合学习中检测和分类帕金森病
Sangeetha Subramaniam1, Umarani Balakrishnan2
1Department of Information Technology, Kongunadu College of Engineering and Technology (Autonomous), Trichy, India.
概括
这项研究引入了一个新的AI框架,用于早期发现和分类帕金森病 (PD). 基于FedL_WSSO的Eff-FDMNet实现了高精度,改善了患者的诊断和结果.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 帕金森病 (PD) 是一种进展性神经退行性疾病.
- 早期诊断对于有效的症状管理和减缓疾病进展至关重要.
研究的目的:
- 提出一个新的框架,FedL_WSSO基于Eff-FDMNet,用于准确的PD检测和分类.
- 为了提高诊断可靠性,利用联合学习和先进的深度学习模型.
主要方法:
- 使用高斯过器和增强的图像预处理.
- 使用ShCNN-Fuzzy-ZFNet进行特征提取,然后进行PD检测.
- 通过WSSO训练的Eff-FDMNet与基于CAViaR的服务器更新进行PD分类.
主要成果:
- 获得了0.927.7的最高精度.
- 获得的平均精度为0.905.5.
- 报告的最低虚假阳性率 (FPR) 为0.082,损失为0.073,MSE为0.213,RMSE为0.461.
结论:
- 开发的基于FedL_WSSO的Eff-FDMNet框架显示了PD检测的高精度和低错误率.
- 这种强大的框架有可能通过可靠和个性化的诊断来提高患者的治疗结果.
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