Tumor Recognition Method Based on Domain Adaptive Algorithm

Abstract

It is extremely important to focus on human life and health and to conduct regular cancer screening.A tumor recognition model based on domain adaptive algorithm is proposed to solve the problem of small number of tumor image data sets and some unlabeled ones.The structure of the backbone network is divided into three networks,feature extractor,domain discriminator and label classifier.The feature extractor extracts the features of source domain and target domain to learn tumor features.Label classifier is used to classify and output tumor images.The domain discriminator determines the source of data features.The label classifier plays a game with the domain discriminator to obtain the data distribution of the source domain and the target domain until the distribution of the source domain and the target domain tends to be consistent in the feature space.Then the classifier can classify the data of the target domain.Experimental results on BreakHis data set show that the average accuracy of the proposed model reaches 87.6%,which improves by 16.2% and 14.1% respectively compared with the two classical domain adaptive methods.The proposed method shows a good performance in the classification of unlabeled data sets.

Keywords

• domain adaptation|tumor recognition|characteristics|labels|image classification