Abstract：Objective To compare the feasibility and effectiveness of the three network models [VGG19 network, multi-task learning,
convolutional block attention module (CBAM)] in the feature classification of traditional Chinese medicine Sha images, and to
illustrate the effectiveness of the module. Methods Firstly, through the analysis of the image data of Sha, the image data was divided
into dark red, red and light red according to the color characteristics, and the shape characteristics were divided into point and sheet.
The experiment included two classification tasks and three classification tasks, and the pictures were divided according to the ratio
of training set∶ verification set∶ test set=8∶1∶1. Then, VGG19 network model was used to train the two tasks respectively, and
the network model was improved as the main task, the idea of multi-task learning model was introduced, and CBAM was added. The
training accuracy and test accuracy were evaluated, and the mean value of color and shape accuracy was set to judge the performance
of the network model. Meanwhile, the final classification accuracy was compared by ablation experiment, and the corresponding
relationship between traditional Chinese medicine Sha image characteristics and syndrome types was analyzed. Results Using the
VGG19 network model as the backbone, an improved network using multi-task learning and incorporating CBAM achieved the
highest classification accuracy. When the reduction rate was 1/8, the batch_size was 8, the best training results were obtained. The
accuracy of color classification was 93.90%, and the accuracy of shape classification was 95.12%. The average accuracy was 94.51%.
Conclusion Based on VGG19 network model, the improved network with multi-task learning and CBAM can achieve good results in
the automatic classification and recognition of traditional Chinese medicine Sha image features, and can accurately judge the human
syndrome type combined with the experience and knowledge of traditional Chinese medicine.
李斌a，李霄a，胡广芹a，张新峰b. 基于改进VGG19的中医背部痧象特征分类研究[J]. 中国医疗设备, 2023, 38(9): 12-16.
LI Bina, LI Xiaoa, HU Guangqina, ZHANG Xinfenga. Research on Classification of Traditional Chinese Medicine Sha Features Based on Improved VGG19. China Medical Devices, 2023, 38(9): 12-16.
