在骨髓细胞学中改善白细胞分类,使用卷积神经网络与对比度增强
Shahid Mehmood1,2, Tariq Shahzad3,4, Muhammad Zubair2
1Department of Computer Science, Bahria University, Lahore, 54000, Pakistan.
Scientific reports
|August 19, 2025
概括
一个新的模型使用先进的图像处理精确地分类白细胞 (WBCs). 这种自动化方法显著提高了血液疾病的诊断准确性,帮助医疗专业人员.
科学领域:
- 免疫学和血液学 免疫学和血液学
- 医学图像分析 医学图像分析
- 计算生物学 计算生物学
背景情况:
- 白血球或白细胞 (WBC) 对于免疫功能和识别各种感染至关重要.
- 精确分类WBC类型对于诊断诸如白血病和免疫系统疾病等疾病至关重要.
- 传统的显微镜方法用于WBC识别是劳动密集型的,并且受观察者变化的影响.
研究的目的:
- 开发一个快速而精确的自动化模型来分类白细胞 (WBCs).
- 与传统方法相比,提高WBC识别的准确性和效率.
- 帮助血液学家和病理学家更有效地诊断血液疾病.
主要方法:
- 利用大量白细胞图像数据集进行训练和测试分类模型.
- 员工转移学习以加快模型培训过程.
- 应用对比度有限的自适应直方图平衡 (CLAHE) 来提高图像质量和模型性能.
主要成果:
- 拟议的白细胞分类模型最初的准确率为81%.
- 整合CLAHE显著提高了模型的整体准确性,达到96.5% (15.5%的改进).
- 改进的模型的性能优于现有的白细胞分类的最先进方法.
结论:
- 图像对比度增强技术,特别是CLAHE,可以明显提高卷积神经网络 (CNN) 模型的性能.
- 开发的自动化WBC分类模型比传统的诊断方法提供了显著的进步.
- 这项技术可以大大帮助早期发现血液疾病,并为治疗策略提供信息.
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