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皮肤病学应用中的多级图像细分使用增强的Secretary Bird优化算法.

Ruba Abu Khurma1, Marwa M Emam2, Falguni Chakraborty3

  • 1Department of Computer Sciences, Faculty of Information Technology and Computer Sciences, Yarmouk University, Irbid, 21163, Jordan. Ruba.abukhurma@yu.edu.jo.

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|November 5, 2025
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概括
此摘要是机器生成的。

这项研究引入了一种增强的优化方法,用于从皮肤状况图像网络 (SCIN) 数据集中的皮肤学图像进行细分. 改进的算法提高了使用AI诊断皮肤疾病的准确性和效率.

关键词:
图像细分 图像细分 图像细分多层次的门持有基于对立的学习 (OBL)正交式学习 (OL) 是一种学习方式.我们的SCINE数据集.秘书鸟类优化算法 (SBOA)

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科学领域:

  • 皮肤学图像分析和人工智能.
  • 医学成像和计算病理学.
  • 生物医学应用的优化算法.

背景情况:

  • 皮肤病在全球面临诊断和治疗方面的挑战.
  • 高分辨率成像和像皮肤状况图像网络 (SCIN) 这样的数据集正在推进皮肤病学.
  • 精确的图像细分对于自动诊断,病变识别和皮肤病理图像的测量至关重要,但由于皮肤纹理,照明和病变外观的变化而面临挑战.

研究的目的:

  • 为了增强多层次的图像细分优化,专门用于SCIN皮肤病图像.
  • 通过使用新的优化技术来提高细分精度,对文物的稳定性和计算效率.
  • 研究精确细分在自动化皮肤病诊断和人工智能驱动的医疗保健解决方案的临床益处.

主要方法:

  • 开发一种针对SCIN数据集图像量身定制的增强多层图像细分优化方法.
  • 将基于对立的学习 (OBL) 和正交的学习 (OL) 集成到秘书鸟优化算法 (SBOA) 中,创建了mSBOA.
  • 应用mSBOA来解决诸如重叠纹理,可变照明和皮肤学图像中的图像工件等挑战.

主要成果:

  • 增强的mSBOA显示了皮肤学图像的细分精度提高.
  • 观察到对常见的图像工件和可变的照明条件的强度提高.
  • 优化的算法实现了更高的计算效率,而不会影响诊断准确度.

结论:

  • 提议的增强优化方法显著改善了SCIN皮肤病图像中的多层次特征细分.
  • 准确的细分方便个性化治疗,提高患者的治疗结果,并减少诊断错误.
  • 这一进步支持人工智能在皮肤病领域的更广泛采用,特别是在服务不足的地区,通过提高自动诊断能力.