Study on Objective Recognition and Color Classification of Sublingual Veins
10.19879/j.cnki.1005-5304.202301297
- VernacularTitle:舌下络脉的客观识别与颜色分类研究
- Author:
Lijuan WANG
1
;
Peng QIAN
;
Shuai YANG
;
Hua XU
;
Fufeng LI
Author Information
1. 上海中医药大学中医学院,上海 201203
- Keywords:
sublingual veins diagnosis;
color feature;
feature extraction;
deep learning
- From:
Chinese Journal of Information on Traditional Chinese Medicine
2024;31(1):147-151
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the method of objective identification of color information in sublingual veins diagnosis of TCM.Methods Combined with computer vision,compact fully convolution networks(CFCNs)and 19 deep learning classification models were used for study,and a double pulse rectangle algorithm was designed as a means of segmentation and recognition of sublingual veins and color information extraction.Results The accuracy of segmentation of tongue bottom obtained by the method of removing reflection + data expanding + data post-processing was 0.955 9,F1 value was 0.947 3,and mIoU value was 0.900 0.The accuracy of segmentation of sublingual veins obtained by the method of removing reflection + tongue input + data expanding + corrosion expansion was 0.778 4,F1 value was 0.738 3 and mIoU value was 0.585 1,which were obviously superior to the current classic or improved U-net model.On the color classification of sublingual veins,the best classification model was DenseNet161-bc-early_stopping with an accuracy rate of 0.803 7.Conclusion The deep learning method has a certain effect on identifying the color information of sublingual veins in TCM,which provides a new method for the research of quantitative color detection technology of sublingual veins diagnosis in TCM.
