Fast Lung Image Segmentation Using Lightweight VAEL-Unet

Abstract

INTRODUCTION: A lightweght lung image segmentation model was explored. It was with fast speed and low resouces consumed while the accuracy was comparable to those SOAT models. OBJECTIVES: To improve the segmentation accuracy and computational efficiency of the model in extracting lung regions from chest X-ray images, a lightweight segmentation model enhanced with a visual attention mechanism called VAEL-Unet, was proposed. METHODS: Firstly, the bneck module from the MobileNetV3 network was employed to replace the convolutional and pooling operations at different positions in the U-Net encoder, enabling the model to extract deeper-level features while reducing complexity and parameters. Secondly, an attention module was introduced during feature fusion, where the processed feature maps were sequentially fused with the corresponding positions in the decoder to obtain the segmented image. RESULTS: On ChestXray, the accuracy of VAEL-Unet improves from 97.37% in the traditional U-Net network to 97.69%, while the F1-score increases by 0.67%, 0.77%, 0.61%, and 1.03% compared to U-Net, SegNet, ResUnet and DeepLabV3+ networks. respectively. On LUNA dataset. the F1-score demonstrates improvements of 0.51%, 0.48%, 0.22% and 0.46%, respectively, while the accuracy has increased from 97.78% in the traditional U-Net model to 98.08% in the VAEL-Unet model. The training time of the VAEL-Unet is much less compared to other models. The number of parameters of VAEL-Unet is only 1.1M, significantly less than 32M of U-Net, 29M of SegNet, 48M of Res-Unet, 5.8M of DeeplabV3+ and 41M of DeepLabV3Plus_ResNet50. CONCLUSION: These results indicate that VAEL-Unet’s segmentation performance is slightly better than other referenced models while its training time and parameters are much less.