In order to correctly diagnose the metastasis of lung cancer

this paper applies deep learning technology to segment the focus area of cervical lymph node ultrasound image of lung cancer patients

and proposes a cascade attention UNet network for ultrasound image segmentation. The cascade structure is a two-stage segmentation network combining attention UNet and EfficientNet. The segmentation model includes one-stage coarse segmentation and two-stage fine segmentation. The encoder uses EfficientNet-B5 as the backbone network. The multi-scale features of the image are taken as the input. A new loss function is proposed

which is suitable for small target and few-shot scenarios. The experimental results show that the proposed cascade structure has excellent network performance in cervical lymph node ultrasonic image segmentation

and the Dice coefficient reaches 0.95

which has better segmentation performance than other UNet methods.