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基于优化的卷积神经网络和改进的黑猩猩优化算法的脑瘤细分.

Ramin Ranjbarzadeh1, Payam Zarbakhsh2, Annalina Caputo1

  • 1School of Computing, Faculty of Engineering and Computing, Dublin City University, Ireland.

Computers in biology and medicine
|November 24, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种优化的卷积神经网络 (CNN),用于使用多个磁共振成像 (MRI) 序列进行脑瘤细分. 该框架在细分瘤方面实现了高精度,改善了诊断和治疗规划.

关键词:
大脑瘤是什么?卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.功能选择 功能选择改进了黑猩猩优化算法.

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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
  • 神经瘤学神经瘤学

背景情况:

  • 准确的脑瘤细分对于诊断和治疗计划至关重要.
  • 多个序列 (T1,Flair,T1ce,T2) 的磁共振成像 (MRI) 为瘤识别提供了多样化的信息.
  • 由于图像照明的变化,细分模糊的瘤边界存在挑战.

研究的目的:

  • 开发一个自动和强大的脑瘤细分框架.
  • 通过使用多种MRI模式来提高细分精度.
  • 使用改进的黑猩猩优化算法 (IChOA) 优化一个卷积神经网络 (CNN).

主要方法:

  • 使用了四个MRI序列图像 (T1,Flair,T1ce,T2) 作为输入.
  • 采用了改进的黑猩猩优化算法 (IChOA) 进行特征选择和CNN超参数优化.
  • 集成了一个支持矢量机 (SVM) 分类器用于初始特征选择.

主要成果:

  • 在BRATS 2018数据集上取得了卓越的表现.
  • 报告了高细分精度,精度为97.41%,回忆率为95.78%,子得分为97.04%.
  • 与现有的脑瘤细分框架相比,证明了更好的结果.

结论:

  • 拟议的ICHOA优化的CNN框架为自动脑瘤细分提供了一个强大的解决方案.
  • 多种MRI模式和先进的优化技术的整合提高了细分的准确性.
  • 这种方法在改善脑瘤的临床诊断和治疗计划方面具有显著的潜力.