Recognition of easily-confused TCM herbs using deep learning

Juei Chun Weng, Min Chun Hu, Kun Chan Lan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 8th ACM Multimedia Systems Conference, MMSys 2017
PublisherAssociation for Computing Machinery, Inc
Pages233-234
Number of pages2
ISBN (Electronic)9781450334891
DOIs
Publication statusPublished - 2017 Jun 20
Event8th ACM Multimedia Systems Conference, MMSys 2017 - Taipei, Taiwan
Duration: 2017 Jun 202017 Jun 23

Publication series

NameProceedings of the 8th ACM Multimedia Systems Conference, MMSys 2017

Other

Other8th ACM Multimedia Systems Conference, MMSys 2017
CountryTaiwan
CityTaipei
Period17-06-2017-06-23

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction
  • Software

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  • Cite this

    Weng, J. C., Hu, M. C., & Lan, K. C. (2017). Recognition of easily-confused TCM herbs using deep learning. In Proceedings of the 8th ACM Multimedia Systems Conference, MMSys 2017 (pp. 233-234). (Proceedings of the 8th ACM Multimedia Systems Conference, MMSys 2017). Association for Computing Machinery, Inc. https://doi.org/10.1145/3083187.3083226