北京生物医学工程2026，Vol.45Issue(2)：153-162,10.DOI:10.3969/j.issn.1002-3208.2026.02.006
基于YOLO神经网络的中药材自动识别模型及其手机应用的开发
Development of a YOLO-based deep learning model for automatic identification of Chinese medicinal materials and its mobile application
摘要
Abstract
Objective The variety of traditional Chinese medicinal ingredients is vast,and in response to the ever-increasing market demand,there is an urgent necessity to integrate intelligent artificial intelligence identification technology to enhance both accuracy and efficiency.Methods Between January 2020 and October 2024,two datasets containing images of 163 types of Chinese medicinal materials were collected.These datasets were used for transfer learning and fine-tuning with YOLO neural network models of varying architectures and sizes.The models'performance was evaluated on validation and test sets using metrics such as accuracy,sensitivity,specificity,precision,area under the ROC curve,and F1 scores.The best-performing model was selected.To improve the model's transparency and interpretability,the gradient-weighted class activation mapping technique was employed.Finally,the model was integrated into a user-friendly mobile application using the Streamlit framework.Results A total of 276 767 images were included in this study,and six YOLO neural network models were developed,namely YOLOv8n,v8s,v8m,v11n,v11s,and v11m.Among them,YOLOv11s performed the best,achieving an accuracy of 98.91%,sensitivity of 98.95%,and specificity of 99.99%on the internal validation set.On the external test set,the model achieved an accuracy of 98.68%,sensitivity of 98.68%,and specificity of 99.99%,demonstrating excellent performance.The developed smartphone application enabled rapid real-time recognition of 163 types of Chinese medicinal herbs,intuitively displaying prediction results and confidence rankings.Conclusions The AI model and mobile application developed using the latest YOLOv11s neural network enable fast and accurate identification of 163 types of Chinese medicinal materials.This provides strong support for physicians in Chinese medicinal material identification tasks and holds promising application prospects.关键词
中药材/网络应用程序/人工智能/YOLO/StreamlitKey words
Chinese medicinal materials/Web application/artificial intelligence/YOLO/Streamlit分类
医药卫生引用本文复制引用
戴一奇,张子豪,奚美娟,夏开建,王甘红,陈健..基于YOLO神经网络的中药材自动识别模型及其手机应用的开发[J].北京生物医学工程,2026,45(2):153-162,10.基金项目
苏州市第二十三批科技发展计划(临床试验机构能力提升)项目(SLT2023006)、苏州市科技攻关计划(医疗卫生创新)项目(SYW2025034)资助 （临床试验机构能力提升）
