使用CNN架构在MRI图像中区分脑中风类型的存在
Srisabarimani Kaliannan1, Arthi Rengaraj1
1Department of ECE, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, India.
Current medical imaging
|February 23, 2024
概括
这项研究使用卷积神经网络 (CNN) 来从MRI扫描中准确检测早期脑中风. 这些模型的准确性高达98%,有助于及时诊断和治疗决策.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 神经学 神经学
背景情况:
- 卒中是全球死亡和残疾的主要原因之一.
- 早期识别中风预警信号对于降低严重程度至关重要.
- 及时诊断和治疗显著改善了患者的治疗结果.
研究的目的:
- 开发和评估卷积神经网络 (CNN) 模型用于早期中风检测.
- 用磁共振成像 (MRI) 序列来区分中风和非中风病例.
- 帮助中风患者及时作出诊断和治疗决策.
主要方法:
- 利用了脑部MRI扫描 (DWI,SWI,GRE,T2 FLAIR) 的实时数据集.
- 预处理的MRI数据包括标准化,正常化和增强.
- 已实施和训练的CNN架构:ResNet,DenseNet,EfficientNet和VGG16. 这三种架构.
主要成果:
- 在区分中风和非中风病例方面取得了高准确性.
- ResNet,DenseNet和EfficientNet模型的准确性达到了98%.
- VGG16模型在中风检测方面实现了97%的准确性.
结论:
- CNN模型显示了精确和早期中风区分的巨大潜力.
- 开发的模型可以帮助临床医生及时做出诊断和治疗决策.
- 这项研究强调了深度学习在神经成像中用于中风识别的有效性.
关键词:
准确性. 准确性. 这就是准确性.深度学习是一种深度学习.在DenseNet中,使用的是DenseNet.有效的网络有效的网络这就是为什么MRI是MRI.这就是ResNet ResNet.冲击差异化的冲击.在VGG16中,VGG16是VGG16中的一个.更多相关视频
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