Conventional methods for identifying traditional Chinese medicine (TCM) herbals mainly rely on subjective experiences, making it difficult to meet the needs for accurate classification and identification.

This study aims to develop an artificial intelligence model and a desktop application capable of automatically recognizing 163 types of TCM herbals based on convolutional neural networks (CNN) .

From January 2020 to June 2024, data from two datasets of 163 TCM herbals were collected for training, validation, and testing of the deep learning model. The performance of the CNN model was evaluated for the accuracy, sensitivity, specificity, precision, area under the receiver operating characteristic (ROC) curve (AUC), and F1 score. After model training, an application was developed using PyQt5 technology for convenient clinical use.

A total of 276 767 images were included in this study. Five models, including EfficientNetB0, ResNet50, MobileNetV3, VGG19, and ResNet18, were developed. After comparing their performance, the EfficientNetB0 model achieved the highest accuracy (99.0%) and AUC (0.994 2) in the validation dataset, and it was selected as the optimal model. In the test dataset, the EfficientNetB0 model achieved an accuracy of 99.0%, sensitivity of 99.0%, specificity of 100.0%, and an AUC of 1.0 across all categories, demonstrating an excellent performance.

The deep learning model developed based on CNN can quickly and accurately recognize 163 types of TCM herbals with high sensitivity and recognition capability, thus providing a robust support for physicians to accurately identify TCM herbals.

传统中药材检测手段依赖主观经验，难以满足中药材在准确分类与鉴别方面的需求。

基于卷积神经网络（CNN）开发一款能够自动识别163种中药材的人工智能模型及电脑端应用程序。

2020年1月—2024年6月，采集了两个中药材数据集进行深度学习模型的训练、验证和测试，共包含163种中药材。通过准确率、灵敏度、特异度、精确率、受试者工作特征（ROC）曲线下面积（AUC）、F1分数等指标来衡量CNN模型的性能。在模型训练完成后，基于PyQt5技术开发了一款应用程序，供临床便携使用。

本研究共纳入了276 767张图像，开发了EfficientNetB0、ResNet50、MobileNetV3、VGG19和ResNet18 5种模型，通过性能比较，EfficientNet_B0模型在验证集上取得了最高的准确率（99.0%）和AUC（0.994 2），被选为最佳模型。在测试集上，最佳模型对所有中药类别识别的准确率为99.0%、灵敏度为99.0%、特异度为100.0%、AUC为1.0，展现出良好的性能。

基于卷积神经网络开发的深度学习模型能够快速准确地识别163种中药材，借助其高灵敏度的识别能力，为医师对中药材的鉴别提供有力辅助。