Research on the Identification of Chinese Herbal Medicine Slices Based on Deep Learning

随着人工智能、计算机操作系统和数据库访问等领域的迅速发展，人们逐渐将人工智能视为中西医学领域的重要辅助工具。在中药研究中，常采用深度学习技术来揭示可以应用于其中的空间结构，并利用这些技术识别有益的中草药和草本复方以及其治疗机制。本文中对控制传统中草药配方外观特征相关性与内涵进行了概括总结，这些特征主要通过外部形态和显微粒径特征加以鉴别。通过采用卷积神经网络(CNN)自动分析与中药特征匹配的化学成分，可以推导活性成分的含量。展望未来，人工智能技术与近红外光谱相结合在中药材及饮片质量评价领域具有广泛应用前景。此方法整体上优势明显，在提高效率、精度等方面表现突出。With the rapid development of artificial intelligence, computer operating system and database access, people gradually regard artificial intelligence as an important auxiliary tool in the field of Chinese and Western medicine. In TCM research, deep learning techniques are often employed to reveal the spatial structures that can be applied to them, and these techniques are used to identify beneficial Chinese herbs and herbal compounds as well as their therapeutic mechanisms. In this paper, the correlation and connotation of the appearance features of controlling traditional Chinese herbal medicine formulations are summarized. These features are mainly identified by external morphology and microscopic particle size characteristics. By using convolutional neural network (CNN) to automatically analyze the chemical components matching the characteristics of traditional Chinese medicine, the content of active ingredients can be derived. Looking forward to the future, the combination of artificial intelligence technology and near-infrared spectroscopy has a wide application prospect in the field of quality evaluation of Chinese medicinal materials and decoction pieces. This method has obvious advantages on the whole, and has outstanding performance in improving efficiency and accuracy.