CA‐UNet: Convolution and attention fusion for lung nodule segmentation

Abstract

Lung cancer is one of the deadliest cancers in the world and is a serious threat to human life. Lung nodules are an early manifestation of lung cancer, early detection and treatment of which can improve the survival rate of patients. In order to accurately segment the lung nodule regions in lung CT images, CA‐UNet, an encoding and decoding structure based on convolution and attention fusion, is proposed based on the U‐Net network. It has improved on two points: First, at the skip connection, the global feature information is extracted using the Swin Transformer block and then fused with the pre‐extraction features and subsequently fed into the corresponding layer of the decoder; second, each channel information is reweighted in the decoder by the channel attention module so that the network focuses on more important channels. Experimental results on the LIDC‐IDRI public database of lung nodules showed that the intersection of union, dice similarity coefficient, precision, and recall of the algorithm were 82.42%, 89.86%, 89.07%, and 92.44%, respectively. The algorithm has better segmentation performance compared to other segmentation methods.