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Study on identification algorithm of traditional Chinese medicinals microscopic image based on convolutional neural network
When the similarity of medicinal materials is high and easily confused, the traditional subjective judgment has an impact on the identification results. Use high-dimensional features to identify medicinal materials to ensure the quality of Chinese herbal concoction products and proprietary Chinese m...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10289762/ https://www.ncbi.nlm.nih.gov/pubmed/37352072 http://dx.doi.org/10.1097/MD.0000000000034085 |
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author | Ma, Yiyi Zhong, Yanmei Su, Qin Xu, Luman Song, Haibei Wen, Chuanbiao |
author_facet | Ma, Yiyi Zhong, Yanmei Su, Qin Xu, Luman Song, Haibei Wen, Chuanbiao |
author_sort | Ma, Yiyi |
collection | PubMed |
description | When the similarity of medicinal materials is high and easily confused, the traditional subjective judgment has an impact on the identification results. Use high-dimensional features to identify medicinal materials to ensure the quality of Chinese herbal concoction products and proprietary Chinese medicines. Objective: To study the identification algorithm of traditional Chinese medicinals (TCM) microscopic images based on convolutional neural network (CNN) to improve the objectivity and accuracy of microscopic image identification of TCM powders. Methods: Microscopic image datasets of 4 TCM powders sclereids of Rhizoma Coptidis, Cortex Magnoliae Officinalis, Cortex Phellodendri Chinensis, and Cortex Cinnamomi were constructed, and 400 collected images, as the model training and testing objects, were identified and classified by AlexNet model, VGGNet-16, VGGNet-19, and GoogLeNet model. Results: The average recognition accuracy in the tested microscopic image of AlexNet model, VGGNet-16, VGGNet-19, and the GoogLeNet model is 93.50%, 95.75%, 95.75%, and 97.50% correspondingly. Conclusion: The GoogLeNet model has a higher classification accuracy and is the best model to achieve real-time. Applying the CNN to the identification of microscopic images of TCM powders makes the operation of TCM identification simpler and the measurement more accurate while improving repeatability. |
format | Online Article Text |
id | pubmed-10289762 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-102897622023-06-24 Study on identification algorithm of traditional Chinese medicinals microscopic image based on convolutional neural network Ma, Yiyi Zhong, Yanmei Su, Qin Xu, Luman Song, Haibei Wen, Chuanbiao Medicine (Baltimore) 3800 When the similarity of medicinal materials is high and easily confused, the traditional subjective judgment has an impact on the identification results. Use high-dimensional features to identify medicinal materials to ensure the quality of Chinese herbal concoction products and proprietary Chinese medicines. Objective: To study the identification algorithm of traditional Chinese medicinals (TCM) microscopic images based on convolutional neural network (CNN) to improve the objectivity and accuracy of microscopic image identification of TCM powders. Methods: Microscopic image datasets of 4 TCM powders sclereids of Rhizoma Coptidis, Cortex Magnoliae Officinalis, Cortex Phellodendri Chinensis, and Cortex Cinnamomi were constructed, and 400 collected images, as the model training and testing objects, were identified and classified by AlexNet model, VGGNet-16, VGGNet-19, and GoogLeNet model. Results: The average recognition accuracy in the tested microscopic image of AlexNet model, VGGNet-16, VGGNet-19, and the GoogLeNet model is 93.50%, 95.75%, 95.75%, and 97.50% correspondingly. Conclusion: The GoogLeNet model has a higher classification accuracy and is the best model to achieve real-time. Applying the CNN to the identification of microscopic images of TCM powders makes the operation of TCM identification simpler and the measurement more accurate while improving repeatability. Lippincott Williams & Wilkins 2023-06-23 /pmc/articles/PMC10289762/ /pubmed/37352072 http://dx.doi.org/10.1097/MD.0000000000034085 Text en Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC) (https://creativecommons.org/licenses/by-nc/4.0/) , where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. |
spellingShingle | 3800 Ma, Yiyi Zhong, Yanmei Su, Qin Xu, Luman Song, Haibei Wen, Chuanbiao Study on identification algorithm of traditional Chinese medicinals microscopic image based on convolutional neural network |
title | Study on identification algorithm of traditional Chinese medicinals microscopic image based on convolutional neural network |
title_full | Study on identification algorithm of traditional Chinese medicinals microscopic image based on convolutional neural network |
title_fullStr | Study on identification algorithm of traditional Chinese medicinals microscopic image based on convolutional neural network |
title_full_unstemmed | Study on identification algorithm of traditional Chinese medicinals microscopic image based on convolutional neural network |
title_short | Study on identification algorithm of traditional Chinese medicinals microscopic image based on convolutional neural network |
title_sort | study on identification algorithm of traditional chinese medicinals microscopic image based on convolutional neural network |
topic | 3800 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10289762/ https://www.ncbi.nlm.nih.gov/pubmed/37352072 http://dx.doi.org/10.1097/MD.0000000000034085 |
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