Skin lesion region segmentation model based on improved U2Net

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Abstract
Skin cancer is one of the most common malignant tumors. In order to completely remove tumors and accurately identify and segment lesion areas, this paper proposes a skin lesion segmentation method based on U2Net. Firstly, deep separable convolution is used to replace conventional convolution for feature extraction of RSU blocks. Embedded branch structures are added at skip connections, and Receptive Field Block(RFB) modules are used to integrate semantic information at different scales. Then, Add Attention Gates (AG) and Normalization-based Attention Modules (NAM), which together form the NAM-Gate Attention mechanism can redistribute image feature weights to reduce the loss of spatial feature information. This article selects Dice, Accuracy, and F1 Score as evaluation indicators and uses the ISIC2018 Task 1 dataset to evaluate our model. The experimental results show that the methods Dice, Accuracy, and F1 Score in this paper have improved by 2.06%, 1.01%, and 1.50%, respectively.