Construction of CNN-based image recognition model for halloysitum rubrum in mineral medicine

Yangbo Sun; Ziwen Huang

doi:10.1117/12.3049654

8 November 2024 Construction of CNN-based image recognition model for halloysitum rubrum in mineral medicine

Yangbo Sun, Ziwen Huang

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134162T (2024) https://doi.org/10.1117/12.3049654
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

Mineral medicines are pivotal in traditional Chinese medicine, with their identification technology being a focal point of research in this field. Over the years, various identification methods for mineral medicines have emerged. Integrating computer vision technology and deep learning theories makes intelligent identification of mineral medicines feasible. This study employs a convolutional neural network (CNN) algorithm to successfully construct an image recognition model for halloysitum rubrum (Chinese medicine name: Chishizhi). Utilizing the TensorFlow deep learning framework, we preprocess and augment a limited dataset of mineral images, substantially expanding the data scale and addressing sample imbalances. Based on image analysis, accuracy, and loss are evaluated as performance metrics for image recognition. The results demonstrate a high recognition accuracy of 92% with a stable loss between 0.1 and 0.2. Furthermore, the optimized model exhibits faster convergence speed and lower training time, markedly enhancing the efficiency of network training and achieving intelligent recognition of halloysitum rubrum images.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yangbo Sun and Ziwen Huang "Construction of CNN-based image recognition model for halloysitum rubrum in mineral medicine", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134162T (8 November 2024); https://doi.org/10.1117/12.3049654

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KEYWORDS

Minerals

Medicine

Education and training

Data modeling

Performance modeling

Artificial neural networks

Image processing

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