RECOGNITION OF TRADITIONAL CHINESE MEDICINES THROUGH TENSORFLOW TECHNIQUES FOR PUBLIC HEALTH ENHANCEMENT
Traditional Chinese Medicines (TCM) have become increasingly popular as a means of disease prevention, diagnosis, and treatment for humans in recent years. However, the lack of easily accessible information and extensive knowledge regarding Chinese herbs presents a challenge. TCM specialists typically rely on manual recognition to identify herbal medicine. This research paper introduces an innovative method for detecting and identifying Chinese herbs using image processing, specifically a tensor flow approach. The study focuses explicitly on three types of Chinese herbs: LianZi, ShanZhiZi, and MoShiZi, which serve as input data. Various experiments were conducted using images of single, paired, and multiple Chinese herbs, as well as a mixture of these varieties. Each category contained thirty test images for each variety of herb. Unbelievably, the results demonstrated a recognition accuracy of up to 87.77%. In addition, the study demonstrated that Chinese herbs were simpler to detect under split conditions than under closing conditions. This paper's innovative image processing approach represents a significant advancement in TCM research and development, providing a reliable and efficient method for identifying Chinese herbs, which will ultimately benefit public health.
Image Detection; Image Recognition; Chinese Medicine; Tensor Flow; Public Health