###### Abstract  
Aiming at the low contrast of skin lesion image and inaccurate segmentation of lesion boundary, a skin lesion segmentation method based on multi-level split receptive field and attention is proposed. Firstly, the depth feature extraction module and multi-level splitting receptive field module are used to extract image feature information; secondly, the hybrid pooling module is used to build long-term and short-term dependencies and integrate global information and local information. Finally, the reverse residual external attention module is introduced to construct the decoding part, which can mine the potential relationship between data sets and improve the network segmentation ability. Experiments on ISBI2017 and ISIC2018 data sets show that the Dice similarity coefficient and Jaccard index reach 88.67% and 91.84%, 79.25% and 81.48%, respectively, and the accuracy reaches 93.89% and 96.16%. The segmentation method is superior to the existing algorithms as a whole. Simulation experiments show that the network has a good effect on skin lesion image segmentation and provides a new method for skin disease diagnosis. image segmentation; receptive field module; mixed pooling module; reverse residual external attention +
Footnote †: journal: Medical  
while the decoder restores the extracted feature map to the original image size and outputs the final segmentation result map. In order to improve the performance of skin disease segmentation, many scholars will improve the encoder-decoder structure and introduce some effective mechanisms to enhance the learning performance of the network, such as attention, residual structure and so on. [PERSON] et al. [7] put forward a U-Net++ network in 2018, which introduced dense connections and added more hop connection paths to make up for the lack of information between codec and decoder. [PERSON] et al. [8] proposed a U-Net3+ network in 2020. In view of the deficiency that U-Net++ did not extract enough information from multiple scales, U-Net3+ used full-scale jump connection and deep supervision to improve this problem. [PERSON] et al. [9] proposed an R2U-Net network architecture, which integrated the structures of U-Net, ResNet [10] and RCNN [11] and achieved good experimental results in many medical image segmentation tasks such as blood vessels, lungs and retina. [PERSON] et al. [12] put forward the Residual Attention Perception Network, which added an attention mechanism to the U-Net network for the first time and used an attention mechanism to fuse low-level feature information and deep feature information, so as to extract the context information of the feature map and improve the segmentation effect. [PERSON] et al. [13] proposed a new segmentation network model by using extended residual and pyramid pooling. The encoding part uses an extended residual network to extract image feature information, and the decoding part uses pyramid pooling to reconstruct image information to obtain a segmented image. Although the above methods can effectively segment the lesion region, there are still some challenges that cannot be ignored, such as small lesions, similar lesion regions to the background, fuzzy edges and so on, which can easily cause the loss of local features of the lesion region in the segmentation process, resulting in poor segmentation results. Figure 1 shows the poor segmentation effect of skin disease images with blurred edges and similar lesion areas in some networks. Because of the lack of feature extraction ability of the input image, the network can not extract deep-level image information, resulting in the loss of feature information, which leads to inaccurate segmentation. In view of the above problems, this paper proposes an efficient skin lesion segmentation method based on multi-level split receptive field and attention. Firstly, the skin lesion images are preprocessed to remove foreign bodies such as hair and blood vessels and reduce their interference with lesion region segmentation.