Evidence map›Paper›PMID 37545883›Full record

ArticleFrontiers in bioengineering and biotechnology2023

Image recognition of traditional Chinese medicine based on deep learning.

Junfeng Miao, Yanan Huang, Zhaoshun Wang, Zeqing Wu, Jianhui Lv

Abstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

  1. CNN-Based Classification ofBiosensors · 2026
    Article
  2. AI-Based Automation for Medication Reconciliation: Scoping Review.Journal of medical Internet research · 2026
    Article
  3. Article
  4. Article
  5. Geographical Origin Identification ofFoods (Basel, Switzerland) · 2025
    Article
  6. Article
  7. Review
  8. A review of recent artificial intelligence for traditional medicine.Journal of traditional and complementary medicine · 2025
    Review
  9. Article
  10. Article
  11. Review
  12. Review
  13. Article
  14. Article
  15. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Junfeng MiaoSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.
Yanan HuangBusiness School, Ezhou Vocational University, Ezhou, Hubei, China.
Zhaoshun WangSchool of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.
Zeqing WuSchool of Pharmacy, Xinxiang Medical University, Xinxiang, China.
Jianhui LvPengcheng Laboratory, Shenzhen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chinese herbal medicine is an essential part of traditional Chinese medicine and herbalism, and has important significance in the treatment combined with modern medicine. The correct use of Chinese herbal medicine, including identification and classification, is crucial to the life safety of patients. Recently, deep learning has achieved advanced performance in image classification, and researchers have applied this technology to carry out classification work on traditional Chinese medicine and its products. Therefore, this paper uses the improved ConvNeXt network to extract features and classify traditional Chinese medicine. Its structure is to fuse ConvNeXt with ACMix network to improve the performance of ConvNeXt feature extraction. Through using data processing and data augmentation techniques, the sample size is indirectly expanded, the generalization ability is enhanced, and the feature extraction ability is improved. A traditional Chinese medicine classification model is established, and the good recognition results are achieved. Finally, the effectiveness of traditional Chinese medicine identification is verified through the established classification model, and different depth of network models are compared to improve the efficiency and accuracy of the model.

Indexed as

classificationdeep learningimage recognitionnetwork modelstraditional Chinese medicinal

Identifiers

PMID37545883
PMCPMC10402920

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.