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使用深度网络融合和最佳特征选择进行MRI驱动的阿尔茨海默病诊断

Muhammad Umair Ali¹, Shaik Javeed Hussain², Majdi Khalid³

¹Department of Artificial Intelligence and Robotics, Sejong University, Seoul 05006, Republic of Korea.

Bioengineering (Basel, Switzerland)

|November 27, 2024

概括

这项研究引入了一种使用脑MRI的自动化阿尔茨海默病检测框架. 混合深度学习方法实现了高精度,有助于早期痴呆症诊断和分期.

科学领域:

神经学神经学
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,导致认知能力下降和痴呆.
由于晚期出现的症状,早期发现AD具有挑战性,但及时干预可以减缓进展.

研究的目的:

开发和评估使用磁共振成像 (MRI) 检测阿尔茨海默病的自动化框架.
提高分类性能,减少特征维度,以提高诊断准确度.

主要方法:

从使用深度学习网络 (EfficientNet-b0,MobileNet-v2) 的脑MRI中提取特征.
通过正规相关性分析 (CCA) 进行特征融合.
使用二进制增强的鱼优化算法 (WOA) 进行最佳功能选择.

主要成果:

与CCA融合的特征在2532个特征中实现了94.7%的分类准确度.
二进制增强的WOA将特征减少到953,获得98.12±0.52%的优异精度.
拟议的方法在统计学上显示出明显的改进 (p < 0.01) 与其他特征选择技术相比.

结论:

混合深度学习和特征选择框架为阿尔茨海默病检测提供了高度准确的自动化方法.
这种方法可以帮助临床医生在痴呆症的早期诊断和分期.
这项研究强调了人工智能在神经退行性疾病诊断中的潜力.

关键词:

阿尔茨海默氏症是阿尔茨海默氏症的疾病准则的相关性分析.深度特征功能功能功能.痴呆症痴呆症是一种痴呆症.功能融合功能融合功能功能选择功能选择机器学习是机器学习.优化的优化优化优化.

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