在基于卷积神经网络和综合评估方法的内镜图像中胃息肉的语义细分
Tao Yan1,2,3, Ye Ying Qin2, Pak Kin Wong2
1School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, China.
Bioengineering (Basel, Switzerland)
|July 29, 2023
概括
卷积神经网络 (CNN) 擅长在内镜图像中细分胃息肉. UNet++与MobineNet v2实现了最佳性能,有助于早期发现胃癌.
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:
- 胃聚体需要准确的识别和切除,因为它们与胃癌的联系.
- 卷积神经网络 (CNN) 显示出对分析内镜图像的前景.
- 使用CNN的胃息肉的自动诊断和细分尚未得到充分研究.
研究的目的:
- 在使用CNN的内镜图像中开创胃息肉细分的研究.
- 为了比较七种经典语义细分模型的性能.
- 为最佳模型选择提出一个综合评估方法.
主要方法:
- 构建并将七个基于CNN的语义细分模型 (U-Net,UNet++,DeepLabv3,DeepLabv3+,PAN,LinkNet,MA-Net) 与ResNet50,MobineNetV2或EfficientNet-B1编码器进行比较.
- 使用了收集的内镜图像数据集.
- 开发了一种综合评估方法,将主观和客观标准结合起来,用于模型选择.
主要成果:
- 使用MobineNet v2编码器的UNet++在综合评估方法的基础上表现出卓越的性能.
- 选择的模型被用来构建一个自动化的多细分系统.
- 该研究证实了语义细分模型在胃息肉诊断中的高临床价值.
结论:
- 基于CNN的语义细分对于胃息肉的诊断和治疗非常有价值.
- 拟议的综合评估方法为在多个复杂模型中进行选择提供了一个客观的工具.
- 这项研究促进了内镜胃肠道疾病的识别,并为临床技术提供了一个模型选择框架.
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