Skin disease migration segmentation network based on multi-scale channel attention

Skin is the first line of defence of the human body. Accurate skin lesions image segmentation is essential for skin disease diagnosis and treatment planning. In this work, we create a new multi-scale channel attention module - MS-CA, which can display more accurate and relevant feature channels on multiple scales. And a network segmentation model for multi-scale channel attention(MS-CA) is proposed. The network model embeds the MS-CA module into two different types of benchmark networks, and modifies the two types of benchmark network models to obtain an image segmentation model suitable for skin diseases. Specifically, by extending the encoder and decoder structures of the LCA-Net network respectively, the MS-CA segmentation model of LCA-Net is proposed, and experiments are carried out on the ISIC2017 and ISIC2018 datasets, and the Dice and accuracy evaluation indicators are improved from 85.33% to 86.71 %, 96.07% increased to 97.23% and the MS-CA segmentation model of TransUNet was proposed. The Jaccard and Dice coefficients were increased from 66.21% to 68.80%, respectively. 79.67% increased to 81.52%. Compared with the existing methods, the MS-CA attention network segmentation model achieves better segmentation results, and it can be used as a new skin disease segmentation network as a computer-aided diagnosis tool.

Automated summary

This research proposes a new multi-scale channel attention module (MS-CA), which is applied to an image segmentation model for accurate diagnosis and treatment planning of skin lesions. Experimental results show that the MS-CA model achieves better segmentation results compared to existing methods.