Study on identification algorithm of traditional Chinese medicinals microscopic image based on convolutional neural network

Abstract

When the similarity of medicinal materials is high and easily confused, the traditional subjective judgment has an impact on the identification results. Use high-dimensional features to identify medicinal materials to ensure the quality of Chinese herbal concoction products and proprietary Chinese medicines. Objective: To study the identification algorithm of traditional Chinese medicinals (TCM) microscopic images based on convolutional neural network (CNN) to improve the objectivity and accuracy of microscopic image identification of TCM powders. Methods: Microscopic image datasets of 4 TCM powders sclereids of Rhizoma Coptidis, Cortex Magnoliae Officinalis, Cortex Phellodendri Chinensis, and Cortex Cinnamomi were constructed, and 400 collected images, as the model training and testing objects, were identified and classified by AlexNet model, VGGNet-16, VGGNet-19, and GoogLeNet model. Results: The average recognition accuracy in the tested microscopic image of AlexNet model, VGGNet-16, VGGNet-19, and the GoogLeNet model is 93.50%, 95.75%, 95.75%, and 97.50% correspondingly. Conclusion: The GoogLeNet model has a higher classification accuracy and is the best model to achieve real-time. Applying the CNN to the identification of microscopic images of TCM powders makes the operation of TCM identification simpler and the measurement more accurate while improving repeatability.