JOURNAL ARTICLE

Enhanced Recognition Approach for Herb Medicine using YOLOv8 in Medical Information Systems.

  • Published In: International Journal of Performability Engineering, 2024, v. 20, n. 12. P. 713 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: Shou-Yu Lee; Yu-Sheng Chu; Tzu-Wei Hsu; I-Hsiang Yu; Wong, W. Eric 3 of 3

Abstract

This study presents the Enhanced Recognition Approach for Herb Medicine (in this paper we focus more on Traditional Chinese Medicine) leveraging advancements in Artificial Intelligence (AI) and Machine Learning (ML). By integrating the YOLOv8 model and TensorFlow Lite optimization, the system enables real-time herb recognition on mobile devices with over 90% accuracy. It addresses inefficiencies in herb identification and knowledge dissemination, offering users detailed information on herb functions, applications, and personalized health recommendations. Through optimized datasets, user-friendly interfaces, and portable deployment, the system promotes TCM culture and supports health management. This innovation lays a foundation for digital and intelligent TCM development, with plans to expand functionalities for broader health applications. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Performability Engineering. 2024/12, Vol. 20, Issue 12, p713
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2024
  • ISSN:09731318
  • DOI:10.23940/ijpe.24.12.p1.713722
  • Accession Number:182087134
  • Copyright Statement:Copyright of International Journal of Performability Engineering is the property of Totem Publisher, Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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