通过混合的卷积和卷积神经网络进行动态内核生成,用于白血病和白细胞分类
Osama M Alshehri1, Ahmad Shaf2, Unza Shakeel3
1Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Najran University, Najran, Kingdom of Saudi Arabia.
Scientific reports
|December 15, 2025
概括
一个新的混合卷积-卷积神经网络 (HICNN) 准确检测白血病并分类白细胞. 这种人工智能模型通过分析细微的细胞差异来改善血液癌症诊断.
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:
- 准确的血液癌症诊断依赖于识别细胞中的微妙形态差异,这是传统显微镜分析的挑战.
- 显微镜图像的自动分析对于提高白血病检测和白细胞 (WBC) 亚型的速度和准确性至关重要.
研究的目的:
- 开发一个混合卷积卷积神经网络 (HICNN) 用于自动化白血病检测和WBC形态分析.
- 通过使用先进的深度学习技术精确分类白血病阶段和WBC亚型来提高诊断准确度.
主要方法:
- 开发了一种新的HICNN架构,集成了用于自适应内核生成的卷积层和用于分层特征提取的卷积层.
- 在混合块内利用并行处理来改善微观图像中微妙的细胞变异的歧视.
- 在白血病分期和WBC亚型数据集上训练并验证了HICNN.
主要成果:
- 实现了高精度:99.5%的白血病分期和98.00%的WBC亚型,超越现有的最先进的模型.
- 证明了模型可靠性,Brier分数低 (0.0019和0.0069) 和最小的类间错误分类 (<2%).
- 在50个时代内观察到稳定的训练趋同,在30个时代时验证准确度超过99%.
结论:
- 在HICNN有效地解决特征歧视和模型校准自动化血液诊断的挑战.
- 这一框架显著有望减少诊断模两可,改善早期发现白血病和相关血液疾病.
- HICNN代表了人工智能驱动的血液病理学诊断工具的重大进步.
关键词:
血液癌症的分类 血液癌症的分类临床诊断可靠性 临床诊断可靠性混合CNN - 卷积网络的混合网络.白血病的分期 (Hema-DA)白血球亚型 (Hema-DB) 的分类微观图像分析 微观图像分析模型的校准 (屏障评分)空间层次特征学习的空间层次特征学习.更多相关视频
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