ing at the shortcomings of existing methods for brain tumor image segmentation, this paper proposes a brain tumor image segmentation algorithm based on an improved convolutional neural network. First, DenseNet and U-net network structures are combined to improve the extraction capability for image features. Second, in order to expand the receptive field of the convolution kernel, the cavity convolution is adopted. Moreover, the segmentation results are further finely segmented and output by a fully connected conditional random field recurrent neural networks, thereby obtaining an accurate brain tumor segmentation region. Experimental results show that compared with traditional deep learning methods, the proposed algorithm has an average Dice up to 91.64%, and has a better improvement in accuracy.