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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
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在MRI中使用优化深度随机图扩张扩散卷积注意力网络进行增强的大脑瘤分类.

Jaswinder Singh1, Manish Bhardwaj2, Analp Pathak3

  • 1School of Computer Science and Engineering, IILM University Greater Noida, India.

Medical physics
|September 25, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的深度学习框架,用于使用MRI扫描进行准确的脑瘤分类. 这种先进的方法实现了高精度,有助于早期诊断和治疗.

关键词:
深度实验室V3+脑瘤分类大脑瘤的分类尖顶的猪优化器混合快速传统双边过器多离散的拉盖尔波段转换为变换.

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算生物学 计算生物学

背景情况:

  • 大脑瘤 (BT) 显著破坏大脑功能,需要早期检测才能有效治疗.
  • 通过MRI准确及时诊断对于成功干预脑瘤病例至关重要.

研究的目的:

  • 利用深度学习和优化开发一种革命性的脑瘤分类方法.
  • 通过一种新的深度随机图扩展扩散卷积注意力网络 (DR2DCAN) 增强瘤识别准确性,并使用顶尖猪优化器 (CPO).

主要方法:

  • 使用混合快速常规双边过器 (HFCBF) 进行MRI预处理,以减少噪音和保护边缘.
  • 瘤区域细分和特征提取使用DeepLabV3+和多离散的拉古埃尔波段变换.
  • 使用DR2DCAN进行分类,随机图形扩散注意力机制,由CPO优化.

主要成果:

  • 该框架在各种MRI数据集上进行了测试,包括质瘤,垂体瘤和脑膜瘤.
  • 与现有方法相比,实现了优越的性能,准确率为98.7%,精度为98.4%,回忆率为98.8%,F1得分为98.6%.
  • 统计分析证实了显著的业绩改善.

结论:

  • 开发的框架显示了大脑瘤分类的高准确性.
  • 它显示出作为临床应用和早期诊断脑瘤的有价值工具的前景.