Skin Melanoma Image Segmentation Based on MultiResUNet-SMIS

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    In order to address the problem of low accuracy of skin melanoma lesion segmentation in existing image segmentation methods, a MultiResUNet-SMIS method is proposed based on existing convolution neural network methods. Firstly, according to the imaging characteristics of skin melanoma, the dilation convolution with different dilation rates is introduced to replace the normal convolution, and the receptive field is expanded on the premise of the same parameters so that the model can segment the lesion at multiple scales. Secondly, spatial and channel attention mechanisms are added to the model to redistribute feature weights, expand the influence of features of interest, and suppress irrelevant features. Finally, by combining Focal loss with Dice loss, a new loss function, i.e., FD loss, is proposed to calculate the regression loss and solve the problem of unbalanced foreground and background pixels, so as to further improve the segmentation accuracy of the network model. The experimental results show that Dice, IoU, and Acc of MultiResUNet-SMIS on the ISIC-2018 dataset have reached 89.47%, 82.67%, and 96.13%, respectively, which are better than the original MultiResUNet and mainstream methods such as UNet, UNet++, and DeepLab V3+ in skin melanoma image segmentation.

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  • Received:November 15,2022
  • Revised:December 23,2022
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  • Online: March 17,2023
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