# An Improved Multi-Scale Feature Fusion for Skin Lesion Segmentation
## 2 Related Works  
In recent years, neural networks have achieved remarkable success in computer vision tasks. Deep learning segmentation models have overcome the limitations of manual feature segmentation and shown great potential in automatic feature learning. The UNet segmentation network has provided a new way and direction for image segmentation, and its excellent performance has made it an important tool for medical field segmentation tasks. Therefore, researchers in this field have been continuously improving this model, developing faster and more accurate segmentation algorithms to help doctors make accurate diagnoses. Now, there have been many studies on improving the UNet model in the aspect of image analysis. [PERSON] et al. [10] proposed UNet++ to overcome the limitations of same-scale feature map fusion alone in UNet. UNet++ combined \"long and short\" skip connections to guide the decoder to restore feature maps, and then input more image features to the decoder to complete multi-scale feature fusion in the network. However, UNet++ cannot fully exploit the information contained in the image. Therefore, [PERSON] et al. [11] introduced the UNet3+ network architecture, which combines different levels of information through full-scale skip connections for target matching at different scales. Additionally, it achieves globally based feature representation by implementing deep supervision. [PERSON] et al. developed the Attention UNet [12] based on the UNet model, which gradually strengthens the weights of local interest regions through self-attention. The network effectively suppresses activation in irrelevant areas, reduces redundant parts of skip connections, and broadens its application range. [PERSON] et al. proposed DoubleUNet [13], which overlays two UNet architectures to achieve good segmentation results in various medical image segmentation tasks using different modes. This network architecture has shown promising results in improving overall segmentation accuracy. Additionally, [PERSON] et al. introduced DRUnet [14], a novel segmentation network that integrates ResNet and DenseNet into the encoder-decodermodules of the UNet model. It achieves high segmentation accuracy with fewer model parameters by exploiting features from both models, thus reducing the negative impact of ResNet's relatively shallow depth while benefiting from DenseNet's rich deep features. [PERSON] et al. (2015) proposed TransUNet, which is a network model that combines Transformer to address the problems of UNet in modeling long-range dependencies. This network can greatly improve the segmentation performance of UNet by up-sampling global context features via a decoder and combining them with high-resolution feature maps to achieve accurate segmentation. [PERSON] et al. (2016) proposed a hybrid transformer architecture named UTNet. The network applies novel self-attention modules in both the encoder and decoder to extend network operations at different levels. Long-range dependencies at different scales are captured while minimizing computational cost, enhancing medical image segmentation. [PERSON] et al. (2017) proposed a segmentation model called DS-TransUNet. Unlike many existing transformer-based solvers, the DS-TransUNet employs a mature dual-scale encoding technique. The mechanism uses a dual-scale encoder to obtain feature representations at different scales, and a good transformer interaction fusion block is designed to efficiently fuse multi-scale information through a self-attentive mechanism. In addition, [PERSON] et al. (2018) proposed a lightweight model called MALUNet, which has the characteristics of a few parameters and low complexity. The model combines four new attention modules with a U-shaped structure, greatly reducing model parameters and computational complexity while improving model performance.