Recognition of Tibetan Medicinal Material Slices Based on Multi-Feature Fusion Combined with Deep Learning Model
- VernacularTitle:基于多特征融合结合深度学习模型的药材切片鉴别
- Author:
Liyuan ZHOU
1
,
2
;
Hongmei GAO
;
Qijun ZHAO
;
Dingguo GAO
Author Information
- Keywords: Feature fusion; Deep learning; Tibetan medicinal materials; Image recognition; Medicinal material slices; Attention mechanism
- From: World Science and Technology-Modernization of Traditional Chinese Medicine 2024;26(1):211-217
- CountryChina
- Language:Chinese
- Abstract: Objective The objective of this study is to improve the accuracy of automatic identification in complex background herbal slice images.The goal is to achieve accurate recognition of herbal slice images in the presence of complex backgrounds.Methods The experiment was conducted on a collected and organized dataset of Tibetan herbal slice images.The RGB,HOG,and LBP features of the slices were analyzed.An improved HOG algorithm was used to fuse multiple features,and a deep learning network was utilized for image recognition.Results The proposed method of multi-feature fusion combined with deep learning achieved an identification accuracy of 91.68%on 3610 Tibetan herbal slice images with complex backgrounds.Furthermore,the average identification accuracy for 20 common traditional Chinese medicine slices,such as Chuan Beimu,Hawthorn,and Pinellia,reached 98.00%.This method outperformed existing methods for identifying herbal slices in complex backgrounds,indicating its feasibility and wide applicability for the identification of other traditional Chinese herbal medicines.Conclusion The fusion of multiple features effectively captures distinguishing characteristics of herbal slices in complex backgrounds.It exhibits high recognition rates for Tibetan herbal slices with complex and heavily occluded backgrounds,and can be successfully applied to the recognition of natural scene-based traditional Chinese herbal medicines and herbal slices.This approach shows promising prospects for practical applications.
