用哈里斯·霍克斯优化 (HHO) 和基于深度学习的方法预测阿尔茨海默病的方法,使用MLP-LSTM混合网络
Raheleh Ghadami1, Javad Rahebi2
1Department of Computer Engineering, Istanbul Topkapi University, 34662 Istanbul, Türkiye.
Diagnostics (Basel, Switzerland)
|February 13, 2025
概括
这项研究引入了一种先进的机器学习 (ML) 和深度学习 (DL) 方法,用于使用MRI扫描准确诊断阿尔茨海默病. 综合方法显著提高了分类准确性,有助于早期检测和干预.
科学领域:
- 神经科学是一个神经科学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 阿尔茨海默病 (AD) 是一种进展性神经退行性疾病,其特点是认知能力下降.
- 通过MRI早期诊断对于有效的医疗干预至关重要.
- 目前的ML/DL方法在MRI中准确区分AD和健康状态时面临挑战.
研究的目的:
- 开发一种集成的ML/DL方法,结合群体智能来增强AD分类.
- 提高使用MRI数据区分健康和AD患者的准确性.
主要方法:
- 从使用卷积神经网络 (CNN) 和灰色水平共发生矩阵 (GLCM) 的AD相关的MRI图像中提取特征.
- 使用哈里斯·霍克斯优化 (HHO) 算法进行特征选择.
- 在ADNI数据集上使用多层感知器 (MLP) 和长短期记忆 (LSTM) 网络进行分类.
主要成果:
- 拟议的综合方法实现了97.59%的高分类精度.
- 在阿尔茨海默病诊断中获得了卓越的灵敏度 (97.41%) 和精度 (97.25%).
- 在诊断性能方面表现优于VGG16,GLCM和ResNet-50等既有模型.
结论:
- 集成的群体智能,ML和DL方法在改善AD诊断方面表现出显著的有效性.
- 先进的特征提取和选择技术是提高医学成像诊断精度的关键.
- 这项研究强调了集成人工智能方法在神经成像诊断工具的发展方面的潜力.
关键词:
阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.哈里斯·霍克斯优化 (HHO) 算法LSTM神经网络是一个神经网络.卷积神经网络 (CNN) 是一种神经网络.磁共振成像 (MRI) 是一种磁共振成像.更多相关视频
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