{"title":"利用深度学习识别易混淆的中药","authors":"Juei-Chun Weng, Min-Chun Hu, Kun-Chan Lan","doi":"10.1145/3083187.3083226","DOIUrl":null,"url":null,"abstract":"Chinese herbal medicine (CHM) plays an important role of treatment in traditional Chinese medicine (TCM). Traditionally, CHM is used to restore the balance of the body for sick people and maintain health for common people. However, lack of the knowledge of the herbs may cause misuse of the herbs. In this demo, we will present a real-time smartphone application, which can not only recognize easily-confused herb based on Convolutional Neural Network (CNN), but also provide relevant information about the detected herbs. Our Chinese herb recognition system is implemented on a cloud server and can be used by the client user via smartphone. The recognition system is evaluated by 5-fold cross validation method and the accuracy is around 96%, which is adequate for real-world use.","PeriodicalId":123321,"journal":{"name":"Proceedings of the 8th ACM on Multimedia Systems Conference","volume":"89 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Recognition of Easily-confused TCM Herbs Using Deep Learning\",\"authors\":\"Juei-Chun Weng, Min-Chun Hu, Kun-Chan Lan\",\"doi\":\"10.1145/3083187.3083226\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Chinese herbal medicine (CHM) plays an important role of treatment in traditional Chinese medicine (TCM). Traditionally, CHM is used to restore the balance of the body for sick people and maintain health for common people. However, lack of the knowledge of the herbs may cause misuse of the herbs. In this demo, we will present a real-time smartphone application, which can not only recognize easily-confused herb based on Convolutional Neural Network (CNN), but also provide relevant information about the detected herbs. Our Chinese herb recognition system is implemented on a cloud server and can be used by the client user via smartphone. The recognition system is evaluated by 5-fold cross validation method and the accuracy is around 96%, which is adequate for real-world use.\",\"PeriodicalId\":123321,\"journal\":{\"name\":\"Proceedings of the 8th ACM on Multimedia Systems Conference\",\"volume\":\"89 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 8th ACM on Multimedia Systems Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3083187.3083226\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 8th ACM on Multimedia Systems Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3083187.3083226","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recognition of Easily-confused TCM Herbs Using Deep Learning
Chinese herbal medicine (CHM) plays an important role of treatment in traditional Chinese medicine (TCM). Traditionally, CHM is used to restore the balance of the body for sick people and maintain health for common people. However, lack of the knowledge of the herbs may cause misuse of the herbs. In this demo, we will present a real-time smartphone application, which can not only recognize easily-confused herb based on Convolutional Neural Network (CNN), but also provide relevant information about the detected herbs. Our Chinese herb recognition system is implemented on a cloud server and can be used by the client user via smartphone. The recognition system is evaluated by 5-fold cross validation method and the accuracy is around 96%, which is adequate for real-world use.