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基于深度学习的两阶段结肠镜聚体检测,包括鱼眼和反射校正

Chen-Ming Hsu^1,2,3, Tsung-Hsing Chen^2,3, Chien-Chang Hsu⁴

¹Department of Gastroenterology and Hepatology, Chang Gung Memorial Hospital Taoyuan Branch, Taoyuan, Taiwan.

Journal of gastroenterology and hepatology

|January 16, 2024

概括

这项研究开发了一种两阶段的深度学习模型,通过纠正扭曲和反射来增强结肠镜图像,显著提高了聚检测精度,以更好地查结肠直肠癌.

科学领域:

医疗成像医学成像
人工智能的人工智能
胃肠病学胃肠病学

背景情况:

结肠镜对于诊断结肠直肠疾病至关重要.
计算机辅助系统有助于在结肠镜图像中检测聚体.
图像扭曲 (鱼眼镜头,反射) 阻碍了聚体的检测.

研究的目的:

提出一个两阶段的深度学习模型来纠正结肠镜图像扭曲和反射.
为了提高结肠镜图像中多检测的准确性.

主要方法:

开发了一个两阶段的深度学习模型.
第一个阶段:卷积神经网络 (CNN) 用于鱼眼扭曲校正和聚体检测.
第二阶段:用于反射校正的生成对抗网络 (GAN),其次是用于检测多重体的CNN.

主要成果:

与未经纠正的图像 (90.8%) 相比,该模型在经过校正的图像 (96.8%) 中获得了更高的准确性.
在Kvasir-SEG数据集上,聚体检测的准确性达到96%.
在ROC曲线下的面积为0.94.4.

结论:

拟议的模型有效地纠正了结肠镜图像中的扭曲和反射.
这有利于结直肠多的临床诊断,并提高了结直肠镜质量.

关键词:

人工智能的人工智能是人工智能.结肠镜检查结肠镜检查结肠直肠多胞体的发生.鱼眼扭曲是指鱼眼的扭曲光的反射反射光的反射.

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