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Mohamed Abd Elaziz1, Diego Oliva2, Alaa A El-Bary3

  • 1Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt; Faculty of Computer Science and Engineering, Galala University, Suze 435611, Egypt; Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates.

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|April 29, 2025
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概括
此摘要是机器生成的。

结肠的早期诊断对于预防结肠直肠癌至关重要. 使用增强的虫优化器的新多级值技术显著提高了多形象细分的准确性.

关键词:
泥甲虫优化器 (DBO) 的使用图像细分 图像细分 图像细分多级值设置多级值设置多胞体图像多胞体图像

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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
  • 计算机视觉 计算机视觉

背景情况:

  • 结肠的早期诊断对于预防结肠直肠癌和改善患者治疗结果至关重要.
  • 目前的多胞体检测方法需要改进,以获得更高的准确性和有效性.
  • 结肠直肠癌通常起源于随着时间的推移可以变成癌症的息肉.

研究的目的:

  • 引入一种先进的多级值技术,用于准确的结肠多形象细分.
  • 增强虫优化器 (DBO) 算法,以提高细分性能.
  • 评估拟议的DCTDBO方法在细分多图像中的有效性.

主要方法:

  • 开发一种新型的多级值技术 (MLTs) 用于聚体图像细分.
  • 使用三角突变 (TMO) 和综合学习 (CL) 运算符,称为DCTDBO,增强泥虫优化器 (DBO).
  • 使用八个多图像对DCTDBO与其他细分方法的性能评估.

主要成果:

  • 该DCTDBO方法在细分结肠多图像方面表现出卓越的性能.
  • 与基本的DBO和其他方法相比,DCTDBO在峰值信号噪声比 (PSNR),结构相似度指数 (SSIM) 和特征相似度 (FSIM) 中取得了更高的分数.
  • 对DCTDBO的平均FSIM,SSIM和PSNR值分别为0.9668,0.99217和28.9338,表明性能显著提高.

结论:

  • 增强的DBO算法,结合TMO和CL,有效地改善了结肠多片图像细分.
  • 拟议的DCTDBO方法为预防性医疗保健中早期和准确的息肉诊断提供了一个有希望的方法.
  • 这种先进的细分技术有助于更好的结直肠癌预后和治疗结果.