用Ocotillo优化驱动的深度学习来进行骨髓细胞学分类
Doaa Sami Khafaga1, El-Sayed M El-Kenawy2,3, Faris H Rizk4
1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
PloS one
|August 29, 2025
概括
这项研究引入了Octillo优化算法 (OcOA) 的深度学习框架,以改善血液癌症的骨髓细胞分类. 通过OCOA增强的模型实现了98.24%的准确性,显著提高了诊断可靠性.
科学领域:
- 医疗诊断
- 计算生物学
- 人工智能
背景情况:
- 通过骨髓涂抹分析手动诊断血液癌症是劳动密集且容易出错的.
- 专家的知识限制了目前的白血病和其他血液癌症的诊断方法.
研究的目的:
- 为准确和高效的骨髓细胞分类开发先进的深度学习框架.
- 使用生物灵感优化算法提高血液检查的诊断可靠性.
主要方法:
- 开发了细胞分类的基线卷积神经网络 (CNN).
- 集成Octillo优化算法 (OcOA) 进行特征选择 (bOcOA) 和超参数优化 (连续OcOA).
主要成果:
- 基线CNN的准确率达到了86.29%.
- 通过OCOA的整合,CNN的精度提高到98.24%,灵敏度达到98.34%,特异性达到98.14%.
- 显著减少了平均分类错误,提高了诊断可靠性.
结论:
- 像OCOA这样的超听觉优化技术可以显著提高临床诊断中的深度学习模型的有效性.
- 拟议的方法为自动化细胞学和血液学查提供了可扩展,可解释和准确的解决方案.
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