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CoTCoNet:一个优化的合变压器-卷积网络,具有适应性图形重建,用于白血病检测.

Chandravardhan Singh Raghaw1, Arnav Sharma2, Shubhi Bansal1

  • 1Department of Computer Science and Engineering, Indian Institute of Technology Indore, Khandwa Road, Simrol, Indore, 453552, Madhya Pradesh, India.

Computers in biology and medicine
|July 7, 2024
PubMed
概括

一个新的合变压器卷积网络 (CoTCoNet) 框架显著提高了白血病分类的准确性. 这种人工智能模型增强了血液涂抹分析,为血液恶性瘤提供了更有效和可靠的诊断工具.

关键词:
急性淋巴细胞白血病 (Acute Lymphoblastic Leukemia) 是一种急性淋巴细胞白血病.细胞分类 细胞分类卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.变压器变压器变压器

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科学领域:

  • 血液学 血液学 血液学
  • 计算生物学 计算生物学
  • 医疗成像医学成像

背景情况:

  • 准确的血液涂抹分析对于诊断白血病和血液恶性瘤至关重要.
  • 手动方法耗时,容易出错,并与细胞分化作斗争.
  • 现有的图像处理方法面临着区分良性和恶性细胞形态的挑战.

研究的目的:

  • 为自动化白血病分类开发一个先进的计算框架.
  • 克服手动分析和传统图像处理技术的局限性.
  • 为了提高血液恶性瘤诊断的准确性和效率.

主要方法:

  • 提出了合变压器卷积网络 (CoTCoNet) 框架.
  • 集成的双特征提取全球和空间模式.
  • 采用基于图形的模块,元启发式优化和白细胞细分/合成.

主要成果:

  • 在一个大数据集 (16,982 个细胞) 上实现了高精度 (0.9894) 和F1-Score (0.9893).
  • 在各种数据集上表现出比最先进的方法更优异的性能.
  • 在多个公共数据集中验证了通用性.

结论:

  • 在白血病分类准确性和效率方面,CoTCoNet提供了显著的进步.
  • 该框架提供了可解释的可视化,有助于临床解释.
  • CoTCoNet显示出在血液学诊断中临床应用的巨大潜力.