深度学习方法对Helicobacter pylori感染的诊断实用性,使用食道胃二透镜图像
Daesung Kang1, Kayoung Lee2, Jinseung Kim2
1Department of Healthcare Information Technology Inje University Gimhae Republic of Korea.
概括
深层卷积神经网络 (DCNNs) 显示出使用胃图像诊断Helicobacter pylori感染的前景. 一个集成的DCNN模型实现了高精度,有助于自动检测这种常见的感染.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 胃肠病学 胃肠病学
背景情况:
- 杆菌感染是全球重要的健康问题.
- 准确和及时的诊断对于有效的治疗和管理至关重要.
- 目前的诊断方法可能是侵入性的或有局限性.
研究的目的:
- 评估深层卷积神经网络 (DCNNs) 的诊断性能,以检测H. pylori感染.
- 评估自动图像分析在H. pylori诊断中的潜力.
主要方法:
- 使用了一组数据集,其中包括13,071张来自食道胃二透镜的胃图像.
- 采用了五个预训练的DCNN架构 (ResNet-101,Xception,Inception-v3,InceptionResnet-v2,DenseNet-201). 在这些架构中,我们可以使用
- 开发了一种组合模型,将高性能DCNN的输出结合起来,并评估包括AUC,特异性和准确性在内的诊断指标.
主要成果:
- 整体DCNN模型实现了0.867的曲线下面面积 (AUC),准确率为80.28%,特异性为78.44%,积极预测值为82.66%,负预测值为77.37%.
- 在集合模型和个别DCNN之间观察到AUC的显著差异.
- 整体模型的AUC因胃区域而异,上体显示最高 (0.858) 和下体显示最低 (0.718).
结论:
- 深度卷积神经网络 (DCNNs) 显示出了Helicobacter pylori感染自动诊断的巨大潜力.
- 开发的DCNN模型提供了一个有前途的工具,用于加强从内镜图像对H. pylori感染的评估.
- 进一步的研究可以完善DCNN模型以提高准确性和更广泛的临床应用.
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