CAT-UNet: Integrating CNN Attention Mechanism and TransUNet for Lung Mass Segmentation

Chest X-ray is one of the most common tests in radiology and plays a vital role in helping physicians spot different chest conditions. This paper proposes a new model called CAT-UNet to segment lung masses in chest X-ray images. The CAT-UNet uses TransUNet as the leading architecture and mixes CNN and transformer as an encoder. The CNN uses ResNet50 as the backbone, embedding the coordinate attention (CA) block and four skip connections of different scales to improve the accuracy of finding shallow features. Vision Transformer (ViT), which applied the transformer structure, was used in our method to enhance the feature representation ability of images, and Atrous Spatial Pyramid Pooling (ASPP) was used to adjust the filter’s field-of-view and control the resolution of features. In the decoder, the Convolutional Block Attention Modules (CBAM) are embedded for upsampling so that the segmentation details can be better optimized. To evaluate the performance and generalizability of the proposed method, we conducted a 3-fold cross-validation experiment using 1914 chest X-ray images labeled by radiologists collected from the Department of Radiology at Dalin Tzu Chi Hospital, Taiwan. Experimental results show that the proposed CAT-UNet achieves 89.06% on Dice, 91.62% on sensitivity, 98.64% on specificity, and 95.15% on accuracy, outperforming U-Net, TransUNet, and Swin-UNet encoders.