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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...

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Autores principales: Ma, Yiyi, Zhong, Yanmei, Su, Qin, Xu, Luman, Song, Haibei, Wen, Chuanbiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2023
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.
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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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