帕雷托优化适应性学习与转移的卷积图像融合阿尔茨海默病的疾病分类
Modupe Odusami1, Rytis Maskeliūnas1, Robertas Damaševičius2
1Faculty of Informatics, Kaunas University of Technology, 51368 Kaunas, Lithuania.
Brain sciences
|July 29, 2023
概括
这项研究表明,帕雷托优化了深度学习,可以将阿尔茨海默病 (AD) 的MRI和PET扫描融合起来. VGG19在整合多式成像以更好地评估疾病方面取得了卓越的表现.
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) 是一种进展性神经系统疾病,影响认知和记忆.
- 多模式成像对于通过揭示大脑变化来监测AD进展至关重要.
- 医学图像融合将来自不同模式的数据结合起来,以提高理解.
研究的目的:
- 探索帕雷托优化的深度学习,以整合MRI和PET图像用于AD诊断.
- 评估VGG11,VGG16,VGG19架构在多模式图像融合中的有效性.
- 用阿尔茨海默病神经成像计划 (ADNI) 数据集评估图像融合技术的性能.
主要方法:
- 使用了已经存在的视觉几何组 (VGG) 架构 (VGG11,VGG16,VGG19).
- 应用形态操作和图像处理用于MRI和PET数据对齐.
- 集成的转移卷积层和帕雷托优化用于超参数调整和特征融合.
主要成果:
- 与VGG16和VGG11相比,VGG19在图像融合方面表现出更好的表现.
- 在ADNI数据集上使用SSIM,PSNR,MSE和Entropy等指标评估性能.
- 在MRI和PET模式下,VGG19在不同疾病阶段 (CN,AD,MCI) 实现了更高的SSIM平均得分.
结论:
- 帕雷托优化的深度学习,特别是VGG19,在阿尔茨海默病研究中有效地融合MRI和PET图像.
- 拟议的方法增强了多式联络成像数据的整合,以改善诊断洞察力.
- 这种方法有望促进对阿尔茨海默病进展的监测和理解.
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