从使用深度学习的组织病理图像对H. pylori感染的分类
Abdullahi Umar Ibrahim1,2, Fikret Dirilenoğlu3, Uğuray Payam Hacisalihoğlu4
1Department of Biomedical Engineering, Faculty of Engineering, Near East University, Nicosia, Cyprus. Abdullahi.umaribrahim@neu.edu.tr.
Journal of imaging informatics in medicine
|February 9, 2024
概括
深度学习模型可以在组织病理学图像中准确检测Helicobacter pylori (H. pylori). ResNet101实现了高精度,为诊断这种常见的细菌感染提供了更快的替代方案.
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
- 传染性疾病 传染性疾病
背景情况:
- 杆菌 (H. pylori) 感染影响全球超过40亿人,导致胃部疾病.
- 目前的H. pylori诊断依赖于活检的劳动密集型体系病理学检查.
- 现有的方法耗时,可能会错过低细菌负载.
研究的目的:
- 评估五个预训练的深度学习模型在组织病理图像中检测H. pylori的有效性.
- 为了比较 EfficientNet-b0,DenseNet-201,ResNet-101,MobileNet-v2 和 Xception 的性能.
- 为了确定支持H. pylori诊断的最有前途的模型.
主要方法:
- 对204张基因病理图像进行二进制分类,分为H. pylori阳性和H. pylori阴性病例.
- 利用了五种不同的预训练的卷积神经网络架构.
- 进行了五次交叉验证,以确保可靠的绩效评估.
主要成果:
- 在评估的模型中,ResNet101表现出优越的性能.
- 而ResNet101的平均准确率为0.920.
- 该模型表现出高灵敏度,特异性,PPV,NPV,F1得分,MCC和科恩的卡帕.
结论:
- 深度学习模型,特别是ResNet101,显示出H. pylori诊断的巨大潜力.
- 即使使用有限的数据集,也可以实现有效的H. pylori检测.
- ResNet101可以帮助病理学家在精确和快速的H. pylori诊断.
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