부산대학교 도서관

Chinese herbal medicine is an important part of traditional Chinese medicine (TCM). With developing of traditional Chinese medicine, the usage of Chinese herbal medicine is growing rapidly. It is essential to identify Chinese herbal medicine correctly since Chinese herbal medicine is used to treat disease. However, identifying Chinese herbal medicine is a hard task because lots of Chinese herbal medicine with different properties displays similar appearance, such as Radix StephaniaeTetrandrae and Radix Paeoniae Alba. Traditional methods of classifying Chinese herbal medicine are low-efficiency and rely on professional medical knowledge. Machine learning methods can reduce the need for professional knowledge in some fields due to its self-learning ability. In this study, a framework, called CNN & BLS, combining the convolutional neural network (CNN) with broad learning system (BLS) for identifying the Chinese herbal medicine, is proposed. Experimental results show the CNN & BLS displays the promising performance for identifying Chinese herbal medicine.