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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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基于修改的帆鱼优化器来诊断脑瘤的最佳极端学习机器.

Saad Ali Amin1, Mashal Kasem Sulieman Alqudah2, Saleh Ateeq Almutairi3

  • 1College of Engineering and IT, University of Dubai, Academic City, 14143, Dubai, United Arab Emirates.

Heliyon
|January 16, 2025
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概括

这项研究引入了一种自动化方法,用于在MRI扫描中检测大脑瘤,使用修改后的极端学习机器和修改后的Sailfish优化器. 这种方法显著提高了医疗成像诊断的准确性和效率.

关键词:
自动化方法自动化方法.大脑瘤是什么?计算机辅助检测系统 计算机辅助检测系统极端学习机器 (ELM) 是一种极端学习机器.磁力共振成像 (MRI) 的图像修改的帆船鱼优化器

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科学领域:

  • 医疗成像医学成像
  • 人工智能在医学中的应用
  • 计算神经科学是一种神经科学.

背景情况:

  • 大脑瘤需要准确有效的检测,以便及时治疗.
  • 磁共振成像 (MRI) 是脑瘤可视化的一个关键模式.
  • 现有的自动检测方法在准确性和效率方面面临挑战.

研究的目的:

  • 提出一个分层的自动化方法来检测脑瘤在MRI.
  • 为了提高大脑瘤诊断的性能,使用改进的极端学习机器与改进的Sailfish优化器集成.
  • 为了提高脑瘤的诊断准确度和缩短诊断时间.

主要方法:

  • 图像预处理技术,以提高MRI质量和减少文物.
  • 使用修改后的极端学习机器 (ELM) 进行瘤分类.
  • 优化ELM使用修改的Sailfish优化器 (MSFO) 以提高性能.
  • 在全脑图谱 (WBA) 数据库上进行验证.

主要成果:

  • 提出的方法在脑瘤检测方面实现了93.95%的高精度.
  • 它的性能优于其他方法,回忆率为100%,特异性为91.38%,F1得分为75.64%.
  • 与端到端和CNN方法相比,证明了更高的效率和准确性.

结论:

  • 层次化的自动化方法显示出在MRI中准确和高效地检测脑瘤的巨大潜力.
  • ELM与MSFO的整合增强了医学成像诊断能力.
  • 这种方法可以帮助医疗保健专业人员迅速和精确地做出脑瘤的治疗决定.