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

doi:10.23940/ijpe.24.12.p1.713722

Abstract

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.

Key words: generative artificial intelligence, deep learning, herb medicine/traditional Chinese medicine recognition, YOLOv8, TensorFlow lite

Shou-Yu Lee, Yu-Sheng Chu, Tzu-Wei Hsu, I-Hsiang Yu, and W. Eric Wong. Enhanced Recognition Approach for Herb Medicine using YOLOv8 in Medical Information Systems [J]. Int J Performability Eng, 2024, 20(12): 713-722.

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