{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T08:45:15Z","timestamp":1782377115596,"version":"3.54.5"},"reference-count":13,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,7,2]]},"abstract":"<jats:p>\u00a0The key step in the intelligence of tongue diagnosis is the segmentation of the tongue image, and the accuracy of the segmented edges has a significant impact on the subsequent medical judgment. Deep learning can predict the class of pixel points to achieve pixel-level segmentation of images, so it can be used to handle tongue segmentation tasks. However, different models have different segmentation effects, and they did not learn the connection between space and channels, resulting in inaccurate tongue segmentation. This paper first discussed the choice of model and loss function and then compared the results of different options to find the better model. Associating the red feature of the tongue is very conducive to segmentation as a feature, this paper tested many methods to try to get the color features of the original image to be paid attention to. Finally, this paper proposed an improved Encoder-Decoder network model to solve the problem based on the results. Start with Resnet as the backbone network, then introduce the U-Net model, and then we fused the attention layer, obtained from the source image through convolution and CBAM attention mechanism, and the feature layer obtained from the last upsampling in U-Net. Experimental results show that: The new, improved algorithm results are 2-3 percentage points higher than the popular algorithm, making it more suitable for tongue segmentation tasks.<\/jats:p>","DOI":"10.3233\/jifs-221411","type":"journal-article","created":{"date-parts":[[2023,4,4]],"date-time":"2023-04-04T11:47:01Z","timestamp":1680608821000},"page":"1473-1480","source":"Crossref","is-referenced-by-count":1,"title":["Tongue image segmentation algorithm based on deep convolutional neural network and attention mechanism"],"prefix":"10.1177","volume":"45","author":[{"given":"Chang","family":"Tian","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Hohai University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanjung","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Hohai University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Hohai University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaofan","family":"Fen","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Hohai University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"issue":"07","key":"10.3233\/JIFS-221411_ref1","first-page":"2447","article-title":"Progress on Objectification Technology of Tongue Inspection in Traditional Chinese Medicine and Discussion on its Application [J]","volume":"23","author":"Cai Yiheng","year":"2021","journal-title":"Modernization of Traditional Chinese Medicine and Materia Medica-World Science and Technology"},{"issue":"S1","key":"10.3233\/JIFS-221411_ref2","first-page":"374","article-title":"Method of Tongue Image Segmentation Based on Luminance and Roughness Information [J]","author":"Wu Wenjun","year":"2006","journal-title":"Journal of System Simulation"},{"issue":"09","key":"10.3233\/JIFS-221411_ref3","first-page":"63","article-title":"Tongue Image Segmentation Method Based on Adaptive Thresholds [J]","volume":"21","author":"Li Danxia","year":"2011","journal-title":"Computer Technology and Development"},{"issue":"04","key":"10.3233\/JIFS-221411_ref4","first-page":"688","article-title":"Tongue Image Segmentation Based on Snake Model and Radial Edge Detection[J]","volume":"14","author":"Fu Zhicheng","year":"2009","journal-title":"Journal of Image and Graphics"},{"issue":"10","key":"10.3233\/JIFS-221411_ref6","first-page":"248","article-title":"Research on Tongue Image Segmentation Algorithm Based on High Resolution Feature [J]","volume":"46","author":"Ma Longxiang","year":"2020","journal-title":"Computer Engineering"},{"key":"10.3233\/JIFS-221411_ref7","doi-asserted-by":"crossref","unstructured":"Huang Xiaodong , et al. Lw-TISNet: Light-Weight Convolutional Neural Network Incorporating Attention Mechanism and Multiple Supervision Strategy for Tongue Image Segmentation[J], Sensing and Imaging 23 (1) (2022).","DOI":"10.1007\/s11220-021-00375-x"},{"key":"10.3233\/JIFS-221411_ref8","doi-asserted-by":"crossref","unstructured":"Lei Li , et al. An iterative transfer learning framework for cross-domain tongue segmentation[J], Concurrency and Computation: Practice and Experience 32 (14) (2020), n\/a\u2013n\/a.","DOI":"10.1002\/cpe.5714"},{"issue":"07","key":"10.3233\/JIFS-221411_ref9","first-page":"46","article-title":"Research on TCM tongue image segmentation based on deep learning [J]","volume":"52","author":"Zhong Zhen","year":"2021","journal-title":"Jiangxi Journal of Traditional Chinese Medicine and Pharmacology"},{"issue":"12","key":"10.3233\/JIFS-221411_ref10","first-page":"2276","article-title":"Survey of Tongue Segmentation in Deep Learning [J]","volume":"15","author":"Liu Huilin","year":"2021","journal-title":"Journal of Frontiers of Computer Science and Technology"},{"issue":"12","key":"10.3233\/JIFS-221411_ref11","first-page":"2364","article-title":"Tongue image segmentation algorithm based on deep convolutional neural network and fully conditional random fields [J]","volume":"45","author":"Zhang Xinfeng","year":"2019","journal-title":"Journal of Beijing University of Aeronautics and Astronautics"},{"issue":"05","key":"10.3233\/JIFS-221411_ref12","first-page":"1005","article-title":"Review on Tongue Image Segmentation Technologies for Traditional ChineseMedicine: Methodologies, Performances and Prospects [J]","volume":"47","author":"Lu Yun-Xi","year":"2021","journal-title":"Acta Automation Sinica"},{"issue":"11","key":"10.3233\/JIFS-221411_ref17","first-page":"3500","article-title":"Tongue image segmentation algorithm based on deep convolutional neural network and fully conditional random fields [J]","volume":"39","author":"Yang Guoliang","year":"2018","journal-title":"Image Segmentation of Skin Lesions Based on Improved Fully Convolution Network"},{"key":"10.3233\/JIFS-221411_ref18","doi-asserted-by":"crossref","unstructured":"Lin TsungYi , Goyal Priya , Girshick Ross , He Kaiming , Dollar Piotr et al. Focal Loss for Dense Object Detection.[J], IEEE Transactions on Pattern Analysis and Machine Intelligence 42 (2) (2020).","DOI":"10.1109\/TPAMI.2018.2858826"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-221411","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:45:34Z","timestamp":1777455934000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-221411"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,2]]},"references-count":13,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.3233\/jifs-221411","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,2]]}}}