基于深度迁移学习的舌象特征分类方法研究

A tongue image classification method based on deep transfer learning

舌象分析是计算机视觉技术在中医望诊的客观化、定量化应用研究中的一个重要课题,其中2个关键步骤是舌体分割和舌象分类。通过级联分类器在原始图像上实现自动舌体定位,再将分割后的舌体图像在GoogLeNet和ResNet上进行深度迁移学习训练,用得到的深度网络对齿痕、裂纹和舌苔厚薄3种主要舌象特征进行分类。从中医医疗机构中获取2245幅舌体图像建立数据集,对齿痕、裂纹和舌苔厚薄3类舌体图像进行分类实验,结果表明,所提方法分类性能优于传统的舌体图像特征分类方法,验证了基于深度迁移学习的舌象特征分类方法的有效性。

Tongue image analysis is an important topic in the objective and quantitative research and application of computer vision technology in the diagnosis and treatment of Traditional Chinese Medicine(TCM),and its two key steps are tongue segmentation and tongue image classification.Cascade classifier is used to automatically segment the tongue region on the original image,and then the segmented tongue image is deeply trained and learned on GoogLeNet and ResNet.The obtained depth network is used to classify toothmarks,cracks and thickness of tongue coating.2245 tongue images obtained from specialized TCM medical institutions are used to build a dataset.In the experiments of classifying the three types of tongue images(toothmarks,cracks and thickness tongue coating),this method has better classification performance than the traditional tongue image feature classification methods.The effectiveness of the tongue feature classification method based on deep transfer learning is verified.