Bibliographic Details
| Title: |
Constructing an Artificial Intelligent Deep Neural Network Battery for Tongue Region Segmentation and Tongue Characteristic Recognition |
| Authors: |
Tian-Xing Yi, Jian-Xin Chen, Xue-Song Wang, Meng-Jie Kou, Qing-Qiong Deng, Xu Wang |
| Source: |
World Journal of Traditional Chinese Medicine, Vol 10, Iss 4, Pp 460-464 (2024) |
| Publisher Information: |
Wolters Kluwer Medknow Publications, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Medicine (General) |
| Subject Terms: |
artificial intelligence, deep learning, tongue characteristic recognition, tongue diagnosis, tongue segmentation, traditional chinese medicine, Medicine (General), R5-920 |
| More Details: |
Objective: This study aimed to construct a two-stage deep learning framework to segment and recognize tongue images and enhance the accuracy and efficiency of artificial intelligence (AI) tongue diagnosis in traditional Chinese medicine (TCM). Materials and Methods: Five hundred and ninety-four tongue images of adequate quality were used to construct AI models. First, a multi-attention UNet model was used for semantic segmentation to distinguish the tongue body from the background. In the second stage, a residual network was employed to classify seven important tongue characteristics. Results: The segmentation model achieved 96.12% mean intersection over union, 98.91% mean pixel accuracy, and 97.15% mean precision. The classification models exhibited robustness across seven distinct characteristics with an overall accuracy >80%. These results indicated that the constructed models have potential applications in TCM. Conclusions: This two-stage approach not only streamlines the analysis of tongue images but also sets a new benchmark for accuracy in medical image processing in the field. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2311-8571 |
| Relation: |
https://journals.lww.com/10.4103/wjtcm.wjtcm_92_24; https://doaj.org/toc/2311-8571 |
| DOI: |
10.4103/wjtcm.wjtcm_92_24 |
| Access URL: |
https://doaj.org/article/5753abd86e264dcf802aef5f31445a50 |
| Accession Number: |
edsdoj.5753abd86e264dcf802aef5f31445a50 |
| Database: |
Directory of Open Access Journals |
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