Study on image detection and target recognition based on traditional Chinese medicine
10.1097/st9.0000000000000096
- Author:
Tianchi MAO
1
;
Xing SUN
1
;
Jiayin ZHU
1
;
An LIU
2
;
Yang LI
1
;
Jingang MA
1
;
Cong GUO
2
Author Information
1. College of Medical Information Engineering, Shandong University of Traditional Chinese Medicine, Ji'nan, China
2. State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing, China
- Publication Type:Journal Article
- Keywords:
Deep learning;
Image detection and target recognition;
Real-time Detection Transformer (RT-DETR);
Traditional Chinese medicine
- From:
Science of Traditional Chinese Medicine
2026;4(1):73-80
- CountryChina
- Language:English
-
Abstract:
Background: Chinese herbal pieces are an essential component of traditional Chinese medicine. Accurate identification and classification of these materials are crucial in clinical practice. Objective: This study aims to enhance the recognition efficiency of Chinese herbal pieces using deep learning technology, while addressing the limitations of traditional manual classification methods in terms of both quality and efficiency. Methods: A comprehensive dataset containing 201 types of Chinese herbal pieces was established. Based on Real-time Detection Transformer (RT-DETR), we designed and integrated a Feature-focused Diffusion Network (FDN), resulting in an improved model termed RT-DETR-FDN. The proposed FDN includes a Feature-focus Module and a feature diffusion mechanism, enabling the model to capture more extensive feature information from Chinese herbal pieces and diffuse it across multiple detection scales. Results: Experimental results show that RT-DETR-FDN achieved a precision of 0.925, a recall of 0.943, and an mAP50-95 of 0.851. In addition, the model was compared with representative You Only Look Once series models commonly used in object detection. Compared with these models, RT-DETR-FDN achieved higher recognition accuracy while maintaining a lightweight architecture. Conclusion: This study integrates deep learning with traditional Chinese medicine, providing a more effective solution for the recognition of Chinese herbal pieces.
