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使用优化神经网络进行阿尔茨海默病检测和分类.

Nair Bini Balakrishnan1, Anitha S Pillai1, Jisha Jose Panackal2

  • 1Department of Computer Science, Hindustan Institute of Technology and Science, Chennai, India.

Computers in biology and medicine
|February 11, 2025
PubMed
概括

这项研究引入了一种新的深度强化学习与火焰优化反复神经网络 (DRL-MFORNN) 用于阿尔茨海默病 (AD) 检测. DRL-MFORNN模型在通过脑MRI扫描识别AD方面取得了很高的准确性.

关键词:
发现阿尔茨海默病的检测深度强化学习的学习.飞火焰优化 飞火焰优化经常性的神经网络.

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科学领域:

  • 神经学 神经学
  • 人工智能的人工智能
  • 医疗成像医学成像

背景情况:

  • 阿尔茨海默病 (AD) 是一种神经退行性疾病,导致认知能力下降.
  • 早期和准确的AD诊断对于有效的治疗和改善患者的结果至关重要.

研究的目的:

  • 开发一种新的方法来检测阿尔茨海默病 (AD) 使用深度强化学习 (DRL) 和火焰优化反复神经网络 (MFORNN).
  • 为了提高脑MRI扫描的AD识别的准确性和效率.

主要方法:

  • 大脑MRI样本进行了预处理,以消除噪音并提高质量.
  • 火焰优化 (MFO) 算法用于从MRI图像中选择特征.
  • 循环神经网络 (RNN) 用于学习时间模式,参数通过深度强化学习 (DRL) 微调.
  • 该框架是使用Python实现的.

主要成果:

  • 拟议的DRL-MFORNN算法实现了高性能指标:准确率为99.31%,精度为99.24%,回忆率为99.43%,f-measure为99.35%.
  • 对比分析表明,拟议技术的性能优于传统分类算法.

结论:

  • DRL-MFORNN方法提供了一个高度准确和高效的阿尔茨海默病检测方法.
  • 这种新的框架显示了在早期阿尔茨海默病诊断中临床应用的巨大潜力.