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基于深度学习的上胃肠道地标分类在无线囊内镜中进行色彩转移增强数据构建和验证

Hyeon-Seo Kim¹, Byungwoo Cho², Jong-Oh Park²

¹Graduate School of Data Science, Chonnam National University, Gwangju 61186, Republic of Korea.

Diagnostics (Basel, Switzerland)

|March 27, 2024

概括

这项研究引入了一种新的方法,用于识别胃肠道上部的解剖学标志,使用无线囊内镜 (WCE). 这种新的方法达到90%以上的准确性,增强了WCE的准确性.

科学领域:

胃肠病学胃肠病学
医疗成像医学成像
人工智能的人工智能

背景情况:

无线囊内镜 (WCE) 越来越被采用,但主要用于小肠成像.
使用WCE的上胃肠道检查预计将随着技术的进步而增长.
有限的研究和数据集存在,用于在上GI WCE中标志性标识.

研究的目的:

开发一种新的方法,使用WCE在上部胃肠道中识别解剖学里程碑.
为上部肠道检查创建一个模拟的WCE数据集.
在这种情况下,评估深度学习模型的准确性,以进行里程碑分类.

主要方法:

使用颜色转移技术创建了上部胃肠道的模拟WCE数据集.
模拟和真实的WCE图像之间的相似性通过使用欧几里德距离测量来验证.
在图像预处理和相似性评估后,DenseNet169深度学习模型被用于解剖学里程碑分类.

主要成果:

开发的方法在使用模拟数据集的上部胃肠道的解剖标志分类中取得了超过90%的准确性.
应用利和细节过器使分类准确度从91.32%提高到94.06%.

结论:

这种新的方法在上肠道 WCE 的解剖学标志分类中表现出高准确度.
模拟数据集和先进的图像处理技术可以显著提高WCE在胃镜中的应用.
这项研究有助于WCE技术的进步,用于更广泛的胃肠道检查.

关键词:

深度学习是一种深度学习.标志分类的地标分类.无线囊内镜无线囊内镜

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