Multitask Analysis Method for Tongue Image Based on Edge Computing
In response to the application scenarios of modernized Traditional Chinese Medicine (TCM) diagnostic and treatment equipment moving towards the user end, an effort has been made to enhance the user-friendliness of TCM diagnostic and treatment devices. This involves introducing the concept of edge co...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10480706/ |
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| _version_ | 1849304241698504704 |
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| author | Tingting Song Bin Liu Fengen Yuan Yunfeng Wang Kang Yu Hao Yang Qiuyan Li |
| author_facet | Tingting Song Bin Liu Fengen Yuan Yunfeng Wang Kang Yu Hao Yang Qiuyan Li |
| author_sort | Tingting Song |
| collection | DOAJ |
| description | In response to the application scenarios of modernized Traditional Chinese Medicine (TCM) diagnostic and treatment equipment moving towards the user end, an effort has been made to enhance the user-friendliness of TCM diagnostic and treatment devices. This involves introducing the concept of edge computing into the mobile tongue diagnosis instrument, shifting the tasks of tongue image acquisition and analysis to portable auxiliary diagnostic devices. To improve the efficiency of edge computing devices in handling tongue image analysis tasks, a multi-task network model based on a lightweight network backbone is proposed. The model utilizes the lightweight feature extraction backbone of MobileNet to provide feature encoding for both the semantic segmentation branch and the multi-label classification branch. The semantic segmentation branch adopts a skip-layer connection structure with multi-scale feature maps, and an attention mechanism is incorporated into the classification branch to fuse the feature maps from the segmentation branch. This achieves tongue segmentation and multi-label classification tasks in a computationally efficient environment. The model achieves a pixel accuracy of 85.3% in semantic segmentation and an accuracy of 95.6% in multi-label classification. The network’s forward propagation speed on edge computing platforms reaches 7 frames per second (FPS). The proposed lightweight network backbone multi-task network model ensures a significant improvement in processing efficiency while maintaining the accuracy of segmentation and classification tasks. Additionally, the model exhibits advantages in terms of quantity and scale, saving both storage and computational resources. It not only enhances the accuracy and efficiency of tongue image real-time analysis in edge computing scenarios but also reduces the processing time, providing excellent precision and inference speed. |
| format | Article |
| id | doaj-art-01e88ca2b632431a84e2162c4f688df3 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-01e88ca2b632431a84e2162c4f688df32025-08-20T03:55:48ZengIEEEIEEE Access2169-35362025-01-011312408612409510.1109/ACCESS.2024.338230310480706Multitask Analysis Method for Tongue Image Based on Edge ComputingTingting Song0Bin Liu1https://orcid.org/0009-0006-6860-5745Fengen Yuan2Yunfeng Wang3https://orcid.org/0000-0003-2738-8706Kang Yu4Hao Yang5https://orcid.org/0000-0002-9564-4815Qiuyan Li6Chinese Academy of Sciences, Institute of Microelectronics, Beijing, ChinaChinese Academy of Sciences, Institute of Microelectronics, Beijing, ChinaChinese Academy of Sciences, Institute of Microelectronics, Beijing, ChinaChinese Academy of Sciences, Institute of Microelectronics, Beijing, ChinaChinese Academy of Sciences, Institute of Microelectronics, Beijing, ChinaChinese Academy of Sciences, Institute of Microelectronics, Beijing, ChinaXiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, ChinaIn response to the application scenarios of modernized Traditional Chinese Medicine (TCM) diagnostic and treatment equipment moving towards the user end, an effort has been made to enhance the user-friendliness of TCM diagnostic and treatment devices. This involves introducing the concept of edge computing into the mobile tongue diagnosis instrument, shifting the tasks of tongue image acquisition and analysis to portable auxiliary diagnostic devices. To improve the efficiency of edge computing devices in handling tongue image analysis tasks, a multi-task network model based on a lightweight network backbone is proposed. The model utilizes the lightweight feature extraction backbone of MobileNet to provide feature encoding for both the semantic segmentation branch and the multi-label classification branch. The semantic segmentation branch adopts a skip-layer connection structure with multi-scale feature maps, and an attention mechanism is incorporated into the classification branch to fuse the feature maps from the segmentation branch. This achieves tongue segmentation and multi-label classification tasks in a computationally efficient environment. The model achieves a pixel accuracy of 85.3% in semantic segmentation and an accuracy of 95.6% in multi-label classification. The network’s forward propagation speed on edge computing platforms reaches 7 frames per second (FPS). The proposed lightweight network backbone multi-task network model ensures a significant improvement in processing efficiency while maintaining the accuracy of segmentation and classification tasks. Additionally, the model exhibits advantages in terms of quantity and scale, saving both storage and computational resources. It not only enhances the accuracy and efficiency of tongue image real-time analysis in edge computing scenarios but also reduces the processing time, providing excellent precision and inference speed.https://ieeexplore.ieee.org/document/10480706/Edge computingmulti-task networksemantic segmentationtongue diagnosisclassification |
| spellingShingle | Tingting Song Bin Liu Fengen Yuan Yunfeng Wang Kang Yu Hao Yang Qiuyan Li Multitask Analysis Method for Tongue Image Based on Edge Computing IEEE Access Edge computing multi-task network semantic segmentation tongue diagnosis classification |
| title | Multitask Analysis Method for Tongue Image Based on Edge Computing |
| title_full | Multitask Analysis Method for Tongue Image Based on Edge Computing |
| title_fullStr | Multitask Analysis Method for Tongue Image Based on Edge Computing |
| title_full_unstemmed | Multitask Analysis Method for Tongue Image Based on Edge Computing |
| title_short | Multitask Analysis Method for Tongue Image Based on Edge Computing |
| title_sort | multitask analysis method for tongue image based on edge computing |
| topic | Edge computing multi-task network semantic segmentation tongue diagnosis classification |
| url | https://ieeexplore.ieee.org/document/10480706/ |
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