【摘要】 目的 探讨中医舌下络脉诊的颜色信息客观识别方法。方法 结合计算机视觉，尝试利用紧凑型全卷积网络（CFCNs）和19种深度学习分类模型等算法进行研究，并设计双络脉矩形算法，作为舌下络脉分割识别和颜色信息提取的手段。结果 应用“去除反光点+数据扩充+数据后处理”方法获取的舌底分割的精确率为0.955 9,F1值为0.947 3、mIoU值为0.900 0，应用“去除反光点+语义分割舌体结果作为输入+数据扩充+后处理边缘膨胀腐蚀”方法获取的舌下络脉分割结果精确率为0.778 4、F1值为0.738 3、mIoU值为0.585 1，均明显优于目前经典的或改进的U-net模型。舌下络脉颜色分类上，DenseNet161-bc-early＿stopping分类模型的效果最佳，准确率达0.803 7。结论 深度学习方法对于识别中医舌下络脉颜色信息具有一定作用，可为中医舌下络脉诊的颜色量化检测技术研究提供新方法。更多还原

【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 19deep 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 postprocessing was 0.955 9, F1 value was 0.947 3, and m IoU 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.更多还原