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RTC_TongueNet:基于DeepLabV3的改进的舌头图像细分模型

Yan Tang¹, Daiqing Tan¹, Huixia Li²

¹Beijing University of Chinese Medicine, Beijing, China.

|March 29, 2024

概括

本研究介绍了RTC_TongueNet,这是一个改进的DeepLabV3模型,用于在传统中医中增强舌头图像细分. 该模型有效地提取了本地和全球特征,超过了准确的舌头细分的现有方法.

科学领域:

计算机视觉计算机视觉
医学成像分析医学成像分析
人工智能在医学中的应用

背景情况:

在中国传统医学 (TCM) 中,自动舌头识别至关重要,但由于细分缺陷而受到阻碍.
现有的方法与网络退化作斗争,无法捕捉全球特征,影响细分精度.

研究的目的:

开发一个改进的舌头图像细分模型,RTC_TongueNet,解决当前方法的局限性.
增强本地和全球特征的提取,以实现更有效的舌头细分.

主要方法:

开发了一个改进的DeepLabV3架构 (RTC_TongueNet),集成变压器和增强的剩余结构.
高效通道注意力 (ECA) 模块被纳入了Atrous空间金字塔聚合 (ASPP) 结构,以改善特征融合.
该模型在两个数据集上与FCN,UNet,LRASPP和DeepLabV3进行了评估.

主要成果:

与基线模型相比,RTC_TongueNet在两个数据集上都表现出优异的性能.
该模型实现了与DeepLabV3.3.0相比,平均交叉点 (MIOU) 增加了0.9-1.0%,平均像素精度 (MPA) 增加了0.3-1.1%.
在两个评估数据集中,RTC_TongueNet模型表现出最佳的细分结果.

结论:

通过利用改进的特征提取和注意力机制,RTC_TongueNet有效地对舌头图像进行细分.
拟议的模型为TCM和相关领域的舌头图像细分提供了实际应用和参考值.

关键词:

深度实验室V3V3 舌头细分的细分方式注意力机制注意力机制改善了残留结构的改善.变压器变压器变压器变压器

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