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基于深度学习的传统中医药分类:一种新的方法

Yafen Kang¹, Mingxiang Wu^2,3, Yunlong Gao^2,3

¹School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, China.

Quantitative imaging in medicine and surgery

|August 11, 2025

概括

这项研究开发了一个智能工具,使用深度学习来分类中国草药 (CHM). 该方法的准确性超过98%,有助于准确识别和促进TCM的发展.

科学领域:

计算生物学是一种计算生物学.
药理学是指药理学,即药理学是指药理学.
人工智能的人工智能是人工智能.

背景情况:

传统中医 (TCM) 和其组成部分,中国草药 (CHM),正在获得全球认可.
准确识别CHM至关重要,但受到市场多样性和产品质量问题的挑战.
需要智能工具来有效地分类广泛的CHM.

研究的目的:

为中国草药 (CHM) 开发智能分类工具.
为应对准确识别和区分众多CHM产品的挑战.
为了利用深度学习来实现自动化CHM分类.

主要方法:

使用ResNet50深度学习框架进行CHM图像识别.
实施了剩余结构以增强网络深度和特征提取能力.
微调了输出层,并使用随机梯度下降来优化参数.

主要成果:

在所有24个分类的CHM类别中实现了高识别精度.
在构建的数据集上实现了超过98%的整体准确率.
在13种特定的草药和沙子坚果类别的私人数据集上,证明了100%的准确性,突出了强度.

结论:

基于深度学习的方法准确地分类复杂的CHM属性.
该工具表现出稳定性和强度,在各种条件下有效.
这一进步可以提高CHM识别,化学分析和个性化医疗.

关键词:

中国草药 (CHM) 是一种中药.在ResNet50中使用ResNet50 深度学习是一种深度学习.图像的分类图像的分类.

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