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Classification of Leukocytes01:30

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...

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修改的鱼算法优化囊神经网络在白血病的图像识别.

Bingying Yao1, Li Chao2, Mehdi Asadi3

  • 1Software Engineering Department, Software Engineering Institute Of Guangzhou, Guangzhou, 510000, China.

Scientific reports
|July 4, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个优化的囊神经网络 (CapsNet),用于从医学图像中准确诊断白血病. 这种新的方法,通过修改版Osprey优化算法 (MOA) 得到了增强,与现有的机器学习技术相比,显示出更高的性能.

关键词:
囊神经网络是一个神经网络.图像的分类图像的分类.在白血病中,白血病.修改后的鱼算法算法优化优化 优化优化

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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:

  • 准确和及时的白血病诊断至关重要.
  • 现有的诊断方法可能在速度或准确性方面存在局限性.
  • 医学图像分析为改善诊断能力提供了潜力.

研究的目的:

  • 开发和评估一种新的,优化的囊神经网络 (CapsNet) 用于白血病诊断.
  • 为了提高CapsNet的性能,使用修改版的Osprey优化算法 (MOA).
  • 将拟议的方法与已建立的机器学习技术进行比较,用于白血病图像分类.

主要方法:

  • 实现一个优化的囊神经网络 (CapsNet) 架构.
  • 整合了Osprey优化算法 (MOA) 的修改版,用于性能调整.
  • 使用ALL-IDB数据集的验证,这是白血病图像分类的标准基准.

主要成果:

  • 与其他方法相比,与MOA一起提出的CapsNet证明了白血病的更高的诊断准确性.
  • 对比分析显示,与MBV2/Res,深度智能卷积,ResNet/GA和SVM/JAYA等模型相比,显著改善.
  • 该方法有效地捕获复杂的图像特征和空间关系,这些特征和空间关系对于诊断至关重要.

结论:

  • 优化的CapsNet方法代表了用于白血病诊断的强大而强大的工具.
  • 这种人工智能驱动的方法有可能提高从医学图像中检测白血病的准确性和效率.
  • 进一步的研究可以探索这种技术在医学诊断中的更广泛应用.