纹理特征的融合应用于H. pylori感染分类从组织病理图像
1Department of Computing, Federal University of São Carlos, São Paulo, Brazil. arbackes@yahoo.com.br.
Journal of imaging informatics in medicine
|May 30, 2025
概括
使用纹理特征在胃活检中对Helicobacter pylori (H. pylori) 的自动化分析实现了高精度. 这种方法提供了一个更快,潜在的更具成本效益的替代方法,用于检测H. pylori的传统组织病理学.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 计算病理学计算病理学
- 细菌学 细菌学是一门学科.
背景情况:
- 杆菌 (H. pylori) 感染影响全球数十亿人,导致严重的胃病.
- 目前的诊断依赖于手动组织病理学分析的活检,这是劳动密集型,耗时,容易出错.
- 传统的H. pylori诊断的成本和可访问性可能是限制因素.
研究的目的:
- 为了评估纹理特征的有效性,在组织病理图像中对H. pylori进行自动二元分类.
- 为了研究使用粒子群优化 (PSO) 结合纹理特征的影响,以提高分类性能.
- 将纹理分析方法的性能与最先进的深度学习技术进行比较.
主要方法:
- 从204张基因病理图像中提取了各种纹理特征,不包括颜色信息.
- 采用粒子集群优化 (PSO) 来优化选定的纹理特征的组合.
- 进行了二元分类,以区分H. pylori阳性和H. pylori阴性病例.
主要成果:
- 纹理分析方法实现了94.61%的高精度和94.47%的F1得分.
- 优化的纹理特征组合显示了精度和回忆之间强大的平衡.
- 提出的方法超过了ResNet-101的性能4.41%.
结论:
- 传统的纹理分析方法在组织病理学中对H. pylori检测仍然具有高度竞争力.
- 自动化纹理特征分析为H. pylori诊断提供了一种有希望,高效和准确的方法.
- 与手工方法相比,这种技术有可能提高诊断速度并降低成本.
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