中草药图像识别分类模型的设计与实现OA

Chinese herbal medicine,as an important component of traditional Chinese medicine,plays a crucial role in the treatment of various diseases.Due to the visual similarity of many Chinese herbal medicines,in order to apply deep learning for their classification and recognition,this study constructed a Chinese herbal medicine classification and recognition model.The construction of the model adopts residual neural network combined with convolutional block attention module,and applies fusion of local binary pattern features and transfer learning strategy to improve the recognition accuracy of the model.The experimental results show that the accuracy,recall,precision,and F1 score of the model in this paper reach 96.88%,96.80%,96.87%,and 96.80%,respectively.