A Novel Method of Chinese Herbal Medicine Classification Based on Mutual Learning
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 po...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-05-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/10/9/1557 |
| _version_ | 1797503793288445952 |
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| author | Meng Han Jilin Zhang Yan Zeng Fei Hao Yongjian Ren |
| author_facet | Meng Han Jilin Zhang Yan Zeng Fei Hao Yongjian Ren |
| author_sort | Meng Han |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T03:55:24Z |
| format | Article |
| id | doaj.art-37ed0da327f44a5ba214c6bd6a5821b4 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T03:55:24Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-37ed0da327f44a5ba214c6bd6a5821b42023-11-23T08:46:08ZengMDPI AGMathematics2227-73902022-05-01109155710.3390/math10091557A Novel Method of Chinese Herbal Medicine Classification Based on Mutual LearningMeng Han0Jilin Zhang1Yan Zeng2Fei Hao3Yongjian Ren4Computer & Software School, Hangzhou Dianzi University, Hangzhou 310018, ChinaComputer & Software School, Hangzhou Dianzi University, Hangzhou 310018, ChinaComputer & Software School, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Computer Science, Shaanxi Normal University, Xi’an 710119, ChinaComputer & Software School, Hangzhou Dianzi University, Hangzhou 310018, ChinaChinese 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.https://www.mdpi.com/2227-7390/10/9/1557Chinese herbal medicineclassificationmutual learningdeep neural network |
| spellingShingle | Meng Han Jilin Zhang Yan Zeng Fei Hao Yongjian Ren A Novel Method of Chinese Herbal Medicine Classification Based on Mutual Learning Mathematics Chinese herbal medicine classification mutual learning deep neural network |
| title | A Novel Method of Chinese Herbal Medicine Classification Based on Mutual Learning |
| title_full | A Novel Method of Chinese Herbal Medicine Classification Based on Mutual Learning |
| title_fullStr | A Novel Method of Chinese Herbal Medicine Classification Based on Mutual Learning |
| title_full_unstemmed | A Novel Method of Chinese Herbal Medicine Classification Based on Mutual Learning |
| title_short | A Novel Method of Chinese Herbal Medicine Classification Based on Mutual Learning |
| title_sort | novel method of chinese herbal medicine classification based on mutual learning |
| topic | Chinese herbal medicine classification mutual learning deep neural network |
| url | https://www.mdpi.com/2227-7390/10/9/1557 |
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