Image recognition of traditional Chinese medicine based on deep learning - Miao, Junfeng; Huang, Yanan; Wang, Zhaoshun; Wu, Zeqing; Lv, Jianhui (Frontiers in Bioengineering and Biotechnology, 2023)
Chinese herbal medicine is an essential part of traditional Chinese medicine and herbalism, and has important significance in the treatment combined with modern medicine. The correct use of Chinese herbal medicine, including identification, identification, and classification, is crucial to the life safety of patients. Recently, deep learning has achieved advanced performance in image classification, and researchers have applied this technology to carry out classification work on traditional Chinese medicine and its products. Therefore, this paper uses the improved ConvNeXt network to extract features and classify traditional Chinese medicine. By adding ACMix and stacked FFN networks to the ConvNeXt network, its feature extraction ability is improved. Through using data processing and data augmentation techniques, the sample size is indirectly expanded, the generalization ability is enhanced, and the feature extraction ability is improved. A traditional Chinese medicine classification model is established, and the good recognition results are achieved. Finally, the effectiveness of traditional Chinese medicine identification is verified through the established classification model, and different depth of convolutional neural network models are compared to improve the efficiency and accuracy of the model.