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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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改进了基于拉丁超立方体采样初始化的鱼优化算法,用于COVID-19X射线多门图像分割的X射线多门图像分割.

Zhen Wang1, Dong Zhao2, Ali Asghar Heidari3

  • 1College of Computer Science and Technology, Changchun Normal University, Changchun, 130032, Jilin, China.

Scientific reports
|June 9, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了CAGWOA,一种增强的鱼优化算法,通过多门图像细分来改进COVID-19诊断. 这种新的方法提高了分析肺部X射线的准确性和效率.

关键词:
在COVID-19 X射线检查中.多门图像细分的多门图像细分.团结情报团队的人群.鱼优化算法 鱼优化算法

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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
  • 计算生物学 计算生物学

背景情况:

  • 图像细分对于COVID-19诊断至关重要,多门方法提供了效率.
  • 像卡普尔的方法和标准鱼优化算法 (WOA) 这样的现有技术在 COVID-19 分区的准确性,稳定性和效率方面存在局限性.
  • 挑战包括稳定性差,效率低,以及当前COVID-19检测门选择的准确性问题.

研究的目的:

  • 为COVID-19诊断开发一个改进的多门图像细分技术.
  • 增强鱼优化算法 (WOA),以克服稳定性,效率和准确性的局限性.
  • 引入一种新的算法,CAGWOA,用于对肺部X射线图像进行高级细分.

主要方法:

  • 引入了一种新的算法:拉丁式超立方采样初始化基于多策略增强的WOA (CAGWOA).
  • 纳入关键策略:COS采样初始化 (COSI) 以实现均的解决方案覆盖,适应性全球搜索 (GS) 以防止停滞,以及全维邻近机制 (ADN) 以实现精确的融合.
  • 通过基准功能测试集和对COVID-19肺X射线图像进行比较实验进行验证.

主要成果:

  • 与现有方法相比,CAGWOA在多值图像细分方面表现优越.
  • 肺部X射线图像的实验结果显示,图像细节的保存更好,细分边界更清晰.
  • 该算法在不同的值水平上表现出适应性,证实了它的有效性.

结论:

  • 通过使用多门图像细分,CAGWOA显著改进了现有的COVID-19诊断方法.
  • 综合策略 (COSI,GS,ADN) 提高了稳定性,效率和准确性.
  • 在检测COVID-19时,CAGWOA为医学图像分析提供了有希望的进步.