【摘要】 针对现有技术难以并行实现舌象多标签的高效分类和识别,难以利用标签间的相关性进行综合分析等问题,提出了一种基于多任务卷积神经网络的舌象分类方法,构建了一种多任务联合学习模型,尝试实现传统中医舌诊中对舌色、苔色、裂纹和齿痕等多个标签的同时辨识。首先,在共享网络层对所有标签进行联合学习,从特征提取的角度自动挖掘和利用标签间的相关性;然后,在不同子网络层分别完成特定类别的学习任务,从而消除多标签分类中的歧义性;最后,训练多个Softmax分类器以实现对所有标签的并行预测。研究表明,所提方法能以端到端的方式同时提取舌象的多个特征并直接进行分类识别,在各分类评价指标上的最低值约为0.96,多任务的总体识别时间为34ms,因此该方法在精度和速度上均具有明显优势。更多还原

【Abstract】 It is difficult to exploit the existing methods to achieve efficient classification and identification of tongue image’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 about34 ms.Therefore,this algorithm has obvious advantages in accuracy and speed.更多还原