SHPNeXt: A Novel Method of Multi-Scale and Variable Resolution AI-Based Tongue Image Segmentation
In the domain of Traditional Chinese Medicine, accurately segmenting tongue images is fundamental for computer-assisted diagnosis. Yet, current models often falter with images of diverse scales and clarity, impeding their widespread application. To address this challenge, we propose SHPNeXt, an inno...
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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/10938541/ |
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| author | Chong-Xiao Peng Zhi-Jun Gao Jin-Huan Wang Xin Yue Yi Li Li-Li Sun Yin-Huan Sun Fu-Quan Du |
| author_facet | Chong-Xiao Peng Zhi-Jun Gao Jin-Huan Wang Xin Yue Yi Li Li-Li Sun Yin-Huan Sun Fu-Quan Du |
| author_sort | Chong-Xiao Peng |
| collection | DOAJ |
| description | In the domain of Traditional Chinese Medicine, accurately segmenting tongue images is fundamental for computer-assisted diagnosis. Yet, current models often falter with images of diverse scales and clarity, impeding their widespread application. To address this challenge, we propose SHPNeXt, an innovative network designed to accurately segment tongue images across different scales and resolutions. This model blends PoolFormer and Hire-MLP to adeptly discern both overarching and nuanced details, ensuring accurate segmentation across varying tongue image sizes. Furthermore, SHPNeXt’s precision was further enhanced by integrating a Nuclear-Norm Non-negative Matrix Factorization (NMF) approach, which robustly counters noise in lower quality images. Experiments on three benchmark datasets demonstrate SHPNeXt’s superior performance, achieving mean Intersection over Union (mIoU) scores of 99.64%, 97.05%, and 96.82%. Balancing efficiency and accuracy, SHPNeXt’s architecture comprises 5.984 million parameters and operates at 1.22 GFLOPs, rendering it an exceptionally effective tool for real-world tongue diagnosis in TCM. The code has been released on github: (<uri>https://github.com/Kuanzhaipcx/SHPNeXt.git</uri>). |
| format | Article |
| id | doaj-art-eccb8ad5ded447f199d47d3975d65090 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-eccb8ad5ded447f199d47d3975d650902025-08-20T01:55:02ZengIEEEIEEE Access2169-35362025-01-0113545045451610.1109/ACCESS.2025.355448710938541SHPNeXt: A Novel Method of Multi-Scale and Variable Resolution AI-Based Tongue Image SegmentationChong-Xiao Peng0https://orcid.org/0009-0009-6636-9779Zhi-Jun Gao1https://orcid.org/0000-0002-9546-0047Jin-Huan Wang2Xin Yue3Yi Li4Li-Li Sun5Yin-Huan Sun6Fu-Quan Du7School of Computer Information and Engineering, Heilongjiang University of Science and Technology, Harbin, ChinaSchool of Computer Information and Engineering, Heilongjiang University of Science and Technology, Harbin, ChinaFirst Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, ChinaSchool of Computer Information and Engineering, Heilongjiang University of Science and Technology, Harbin, ChinaSchool of Computer Information and Engineering, Heilongjiang University of Science and Technology, Harbin, ChinaSchool of Computer Information and Engineering, Heilongjiang University of Science and Technology, Harbin, ChinaSchool of Computer Information and Engineering, Heilongjiang University of Science and Technology, Harbin, ChinaSchool of Computer Information and Engineering, Heilongjiang University of Science and Technology, Harbin, ChinaIn the domain of Traditional Chinese Medicine, accurately segmenting tongue images is fundamental for computer-assisted diagnosis. Yet, current models often falter with images of diverse scales and clarity, impeding their widespread application. To address this challenge, we propose SHPNeXt, an innovative network designed to accurately segment tongue images across different scales and resolutions. This model blends PoolFormer and Hire-MLP to adeptly discern both overarching and nuanced details, ensuring accurate segmentation across varying tongue image sizes. Furthermore, SHPNeXt’s precision was further enhanced by integrating a Nuclear-Norm Non-negative Matrix Factorization (NMF) approach, which robustly counters noise in lower quality images. Experiments on three benchmark datasets demonstrate SHPNeXt’s superior performance, achieving mean Intersection over Union (mIoU) scores of 99.64%, 97.05%, and 96.82%. Balancing efficiency and accuracy, SHPNeXt’s architecture comprises 5.984 million parameters and operates at 1.22 GFLOPs, rendering it an exceptionally effective tool for real-world tongue diagnosis in TCM. The code has been released on github: (<uri>https://github.com/Kuanzhaipcx/SHPNeXt.git</uri>).https://ieeexplore.ieee.org/document/10938541/Tongue image segmentationSHPNeXtmulti-scale variable resolution imagesdeep learning |
| spellingShingle | Chong-Xiao Peng Zhi-Jun Gao Jin-Huan Wang Xin Yue Yi Li Li-Li Sun Yin-Huan Sun Fu-Quan Du SHPNeXt: A Novel Method of Multi-Scale and Variable Resolution AI-Based Tongue Image Segmentation IEEE Access Tongue image segmentation SHPNeXt multi-scale variable resolution images deep learning |
| title | SHPNeXt: A Novel Method of Multi-Scale and Variable Resolution AI-Based Tongue Image Segmentation |
| title_full | SHPNeXt: A Novel Method of Multi-Scale and Variable Resolution AI-Based Tongue Image Segmentation |
| title_fullStr | SHPNeXt: A Novel Method of Multi-Scale and Variable Resolution AI-Based Tongue Image Segmentation |
| title_full_unstemmed | SHPNeXt: A Novel Method of Multi-Scale and Variable Resolution AI-Based Tongue Image Segmentation |
| title_short | SHPNeXt: A Novel Method of Multi-Scale and Variable Resolution AI-Based Tongue Image Segmentation |
| title_sort | shpnext a novel method of multi scale and variable resolution ai based tongue image segmentation |
| topic | Tongue image segmentation SHPNeXt multi-scale variable resolution images deep learning |
| url | https://ieeexplore.ieee.org/document/10938541/ |
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