分类意识:精子形态分类的两阶段分割和组合框架
Aydın Kağan Turkoglu1, Gorkem Serbes2, Hakkı Uzun3
1Department of Computer Engineering, Faculty of Electrical and Electronics, Yildiz Technical University, 34220 Istanbul, Turkey.
Diagnostics (Basel, Switzerland)
|September 13, 2025
概括
一个新的两阶段深度学习框架自动化了精子形态分类,提高了男性不孕症诊断的准确性. 这种自动化系统减少了主观性,提高了评估精子健康的可靠性.
科学领域:
- 生殖生物学 生殖生物学
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
背景情况:
- 精子形态评估对于男性不孕症诊断至关重要,但它是主观的,并且取决于操作者.
- 由于生物变异和解释偏差,传统的手工方法缺乏准确性.
- 需要自动化系统来提高精子分析的客观性和可靠性.
研究的目的:
- 开发一个双阶段,完全自动化的框架,用于精子形态分类.
- 为了减少主观性和错误分类在识别精子异常.
- 加强男性生殖健康保健的诊断支持.
主要方法:
- 一个新的两阶段深度学习框架,使用三个染色协议的18个类数据集.
- 第1阶段:分类为头部/部异常与正常/尾部异常.
- 第二阶段:使用结构化的多阶段投票策略,与NFNet-F4和视觉变压器 (ViT) 变体组合模型.
主要成果:
- 该框架在染色协议中实现了69.43%,71.34%和68.41%的精度.
- 与现有文献方法相比,在统计学上有显著的4.38%的改善.
- 在视觉上相似的精子异常类别中大大减少了错误分类.
结论:
- 拟议的基于组合的两阶段框架为自动化精子形态分类提供了强大的解决方案.
- 层次分类和结构化决策融合使自动化分析超越传统方法.
- 为男性生育能力评估的临床决策提供了可靠和可扩展的工具.
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