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基于性别的阿尔茨海默氏症检测使用ResNet-50和神经成像上的二进制龙算法

Muhammad Ikram Ul Haq^1,2, Waqas Haider Bangyal³, Arfan Jaffar¹

¹Department of Software Engineering, Superior University, Lahore, Pakistan.

Frontiers in artificial intelligence

|December 24, 2025

概括

本研究引入了基于性别的阿尔茨海默病检测模型,使用fMRI数据和机器学习. 该GRDN模型在识别男性阿尔茨海默氏症时实现了94.8%的准确性,突出了性别在诊断中的重要性.

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,具有无声发作,使得早期诊断具有挑战性.
现有研究表明,AD风险因性别,年龄,种族和种族而异,但缺乏基于性别的研究.
精确诊断阿尔茨海默氏症需要复杂的算法来检测微妙的大脑变化.

研究的目的:

提出和评估基于性别的阿尔茨海默病检测模型 (GRDN) 使用功能磁共振成像 (fMRI).
通过机器学习探索性别对阿尔茨海默病检测准确性的影响.
通过数据平衡技术,提高对代表性不足的群体的分类器性能.

主要方法:

利用了来自阿尔茨海默病神经成像计划 (ADNI) 数据集的fMRI数据.
使用生成对抗网络 (GAN) 进行数据平衡,并使用ResNet-50架构进行特征提取.
应用了二进制龙算法 (BDA) 进行特征选择,其次是五个机器学习分类器.

主要成果:

增加的特征集大小与更好的分类准确性相关.
fineKNN分类器在男性组中实现了94.8%的高精度,其特征集为450.
拟议的模型在不同研究组中表现一致,优于其他模型.

结论:

GRDN模型显示了基于性别的阿尔茨海默氏症检测的巨大潜力.
特性工程和选择对于提高阿尔茨海默氏症诊断精度至关重要.
对性别特异性风险因素和诊断方法的进一步研究是有必要的.

关键词:

阿迪尼阿迪尼是什么意思阿尔茨海默氏症的疾病是阿尔茨海默氏症.功能磁力共振成像 (fMRI) 是一种性别性别性别性别性别预先训练有素的人.

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