基于噪声样本渐近修正的中医舌色分类方法OA北大核心CSTPCD

Auto tongue color classification is an important research topic in the study of TCM(Traditional Chinese Medicine)objectification.Affected by various factors such as doctor's experience and illumination conditions,there often exist errors in the manually annotated labels,that is,noisy labels.Noisy labels will cause the model not to converge in the training process and the generalization ability will be poor.Therefore,in this paper,a TCM tongue color classification method is proposed by progressively correcting noisy samples.First,according to the characteristics of the tongue color classification,a global-local feature fusion method is proposed,which is embedded in the ResNet18 backbone network,con-structing a tongue color classification network.The ensemble learning paradigm is adopted to improve the reliability and stability of the classification model.Next,for the classification network training problem under noisy samples,a sample at-tention mechanism and a re-labeling mechanism are proposed.During the training process,different weights are assigned to clean samples and noisy samples,and the noisy samples are gradually adjusted.Finally,the network model is optimized and trained with the Boostrapping loss function to suppress the impact of noisy samples on the classification performance.The experimental results on two tongue color classification datasets SIPL-A and SIPL-B show that,the proposed method can effectively correct noisy labels,thereby,significantly improving the tongue color classification accuracy.Compared with the existing image classification methods under noisy samples,the proposed method can achieve a higher classification accuracy,reaching 94.6%and 93.65%,respectively.