A Novel Method of Chinese Herbal Medicine Classification Based on Mutual Learning

Meng Han, Jilin Zhang, Yan Zeng, Fei Hao and Yongjian Ren
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Meng Han: Computer & Software School, Hangzhou Dianzi University, Hangzhou 310018, China
Jilin Zhang: Computer & Software School, Hangzhou Dianzi University, Hangzhou 310018, China
Yan Zeng: Computer & Software School, Hangzhou Dianzi University, Hangzhou 310018, China
Fei Hao: School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
Yongjian Ren: Computer & Software School, Hangzhou Dianzi University, Hangzhou 310018, China

Mathematics, 2022, vol. 10, issue 9, 1-13

Abstract: Chinese herbal medicine classification is an important research task in intelligent medicine, which has been applied widely in the fields of smart medicinal material sorting and medicinal material recommendation. However, most current mainstream methods are semi-automatic, with low efficiency and poor performance. To tackle this problem, a novel Chinese herbal medicine classification method based on mutual learning has been proposed. Specifically, two small student networks are designed for collaborative learning, and each of them collects knowledge learned from the other one respectively. Consequently, student networks obtain rich and reliable features, which will further improve the performance of Chinese herbal medicinal classification. In order to validate the performance of the proposed model, a dataset with 100 Chinese herbal classes (about 10,000 samples) was utilized and extensive experiments were performed. Experimental results verify that the proposed method is superior to those of the latest models with equivalent or even fewer parameters, specifically, obtaining 3?5.4% higher accuracy rate and 13?37% lower loss. Moreover, the mutual learning model achieves 80.8% Chinese herbal medicine classification accuracy.

Keywords: Chinese herbal medicine; classification; mutual learning; deep neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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