通过卷积神经网络的手段通过视觉解释性来评估阿尔茨海默病
Francesco Mercaldo1,2, Marcello Di Giammarco2,3, Fabrizio Ravelli1
1Department of Medicine and Health Sciences "Vincenzo Tiberio", University of Molise, Campobasso, Italy.
International journal of neural systems
|January 26, 2024
概括
这项研究引入了深度学习模型,用于使用脑部扫描早期检测阿尔茨海默病. 新型卷积神经网络在诊断和定位疾病方面取得了高准确性,有助于早期干预.
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:
- 阿尔茨海默病是导致痴呆的主要原因,需要及时干预的早期诊断.
- 阿尔茨海默病的渐进性导致患者的依赖性和家庭的重大负担.
- 早期检测对于在疾病发育阶段进行干预至关重要.
研究的目的:
- 提出一种基于深度学习的方法,用于自动检测阿尔茨海默病.
- 将脑图像分为非痴呆,非常轻度痴呆和轻度痴呆的类别.
- 视觉突出阿尔茨海默病受影响的大脑区域,以诊断解释性.
主要方法:
- 在图像分类中使用了五个卷积神经网络 (ALEX_NET,VGG16,FAB_CONVNET,STANDARD_CNN,FCNN).
- 使用Grad-CAM用于定位和视觉解释诊断结果.
- 在6000多张磁共振图像上进行实验.
主要成果:
- 拟议的STANDARD_CNN和FCNN模型实现了高诊断准确度,精度和回忆 (95%-98%).
- 与最先进的模型相比,在阿尔茨海默氏症诊断和定位方面表现出卓越的性能.
- 在本地化和视觉解释方面,Grad-CAM提供了出色的定性结果.
结论:
- 深度学习模型有效地使用脑成像来诊断和定位阿尔茨海默病.
- 确定了关键的大脑区域,如心室和大脑皮质,表明阿尔茨海默病.
- 未来的研究将探索分析多个解剖视图同时进行增强诊断.
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