Abstract: It is difficult to exploit the existing methods to achieve efficient classification and identification of tongue ima-ge’ labels in parallel,and it is also difficult to utilize the correlation between labels for comprehensive analysis.Aiming at the problems above,this paper proposed a classification method of tongue image based on multi-task deep convolutional neural network and constructed a multi-task joint learning model based on deep convolutional neural network to realize the simultaneous identification of tongue color,moss color,tongue crack and tooth marks in tongue diagnosis of Chinese medicine.First,the shared network layer is used to learn all labels,and the correlation between the tags is extracted and utilized automatically from the perspective of feature extraction.Then,the learning tasks of specific labels are completed in different sub-network layers to eliminate the ambiguity in the multi-label classification problem.Finally,multiple Softmax classifiers are trained to achieve parallel prediction of all labels.Experimental results suggest that the proposed method can simultaneous extract multiple features of tongue image and implement classification by means of end to end.The lowest value is about 0.96 in several evaluation indexes and the multi-task recognition rate is about 34ms.Therefore,this algorithm has obvious advantages in accuracy and speed.

Key words: Correlation, Multi-label, Multi-task network, Tongue classification, Transfer learning