CA‐Unet++: An improved structure for medical CT scanning based on the Unet++ Architecture
Bo Li, Fei Wu, Sikai Liu, Jinhong Tang, Guanghui Li, Meiling Zhong, Xiao‐Hui Guan
Abstract
Currently, deep learning has become more and more mature in the field of medical image segmentation. Through using the computer, the deep learning models established can completely help doctors to perform medical image segmentation. Most of the current deep learning models are based on Unet. The U-shaped structure and the skip connection layer of Unet can effectively achieve precise image segmentation. However, for complicated images, the network structure of Unet is not sufficient enough. In response to this problem, some scholars have designed Unet++ by adding a denser skip connection layer to U-Net. Compared to U-Net, Unet++ is more effective in dealing with complex images, but it has drawbacks in many aspects, and there is still a large loss of eigenvalues in the skip connection and up-sampling processes. To address these issues, this paper uses the channel and attention mechanism to improve the Unet++ model to obtain better image segmentation efficiency and accuracy. Meanwhile, based on Unet++, this paper designs a new model called CA-Unet++. The proposed model uses the channel module and the attention module to solve the eigenvalues loses in the long-distance skip connection process and the up-sampling process, respectively. The experimental results and data analysis shows that our proposed CA-Unet++ can achieve better performance in medical computed tomography scan image segmentation.