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Updated: Jan 6, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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一个高效的深度学习网络用于脑中风检测,使用salp混合牧羊优化优化.

Xingsi Xue1,2, S E Viswapriya3, D Rajeswari4

  • 1College of Artificial Intelligence, Yango University, Fuzhou, 350015, Fujian, China.

Scientific reports
|September 29, 2025
PubMed
概括
此摘要是机器生成的。

一个新的深度学习模型,S3ET-NET,使用MRI扫描精确检测脑中风. 这种方法实现了99.41%的可靠性,改善了缺血性和出血性中风的早期诊断.

关键词:
大脑中风 脑中风 脑中风有效的净效率.高斯斯的双边关系幽灵网是一个幽灵网.沙 混合 牧羊 放牧

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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:

  • 脑中风 (BS) 是关键的脑血管疾病,也是导致死亡的主要原因.
  • 由于大小,形状和位置的变化,出血和缺血性中风存在诊断挑战.
  • 在磁共振成像 (MRI) 中的扩散加权成像 (DWI) 提供了早期检测流体平衡变化,超过了计算机断层扫描 (CT) 的准确性.

研究的目的:

  • 推出一种新的深度学习模型,Salp Shuffled Shepherded EfficientNet (S3ET-NET),用于使用MRI进行自动脑中风检测.
  • 提高脑中风分类的准确性和可靠性,区分正常,缺血性中风 (IS) 和出血性中风 (HS).

主要方法:

  • 用高斯双边 (GB) 过器预处理MRI图像,以尽量减少噪音.
  • 使用幽灵网模型进行特征提取.
  • 最佳特征是通过Salp混合牧羊优化 (S3O) 算法来选择的.
  • 脑中风类型的分类是使用高效净模型进行的.

主要成果:

  • 在S3ET-NET模型中,可靠性率高达99.41%.
  • 与链接网,移动网和谷歌网相比,幽灵网组件显示了更好的检测准确性.
  • 有效净分类器在准确性方面表现优于ResNet50,zNet-mRMR-NB和DNN.

结论:

  • 拟议的S3ET-NET模型提供了一个高度可靠和准确的方法来通过MRI扫描来检测脑中风.
  • 幽灵网和高效网与S3O算法的集成显著提高了诊断性能.
  • 这种深度学习方法有望改善各种类型中风的早期和准确诊断.