基于半监督方法的脑梗死图像识别

在图像识别领域,针对有监督方法的模型在标签数据不足时图像的识别效果不佳问题,提出一种基于生成对抗网络(GAN)的半监督方法模型,即结合了半监督生成对抗网络(SSGAN)和深度卷积生成对抗网络(DCGAN)的优点,并在输出层用softmax代替了sigmoid激活函数,从而建立半监督深度卷积生成对抗网络(SS-DCGAN...

Cerebral infarction image recognition based on semi-supervised method

In the field of image recognition, images with insufficient label data cannot be well recognized by the supervised method model. In order to solve this problem, a semi-supervised method model based on Generative Adversarial Network(GAN) was proposed. That is, by combining the advantages of semi-supervised GANs and deep convolutional GANs, and replacing the sigmoid activation function with softmax in the output layer, the Semi-Supervised Deep Convolutional GAN(SS-DCGAN) model was established. Firstly, the generated samples were defined as pseudo-samples and used to guide the training process. Secondly, the semi-supervised training method was adopted to update the parameters of the model. Finally, the recognition of abnormal(cerebral infarction) images was realized. Experimental results show that the SS-DCGAN model can recognize abnormal images well with little label data, which achieves 95.05% recognition rates. Compared with Residual Network 32(ResNet32) and Ladder networks, the SS-DCGAN model has significant advantages.