Mpox Virus Image Segmentation Based on Multiscale Expansion Convolution

Abstract

A novel strategy for segmenting mpox virus images is proposed to address the challenge of distinguishing lesion areas from muscle tissue and other regions of infection. This strategy leverages a multiscale inflated convolutional feature fusion and attentional Swin-Unet approach. In this method, a multi-scale extended convolution module is employed in the coding stage of the Swin-Unet network to enhance complementary features while preserving different features at different scales. Additionally, a triple attention module is integrated into the downsampling process to address the issue of inter-channel independence. Finally, the Swin Transformer Block is utilized to modulate the network segmentation performance by adjusting the iteration count in the encoding and decoding regions. Experimental results on a self-constructed mpox dataset demonstrate that the proposed network achieves a pixel segmentation accuracy of 90.4% and an average intersection-over-union ratio of 80.3%. These values represent improvements of 8.6% and 14.6%, respectively, compared to the original Swin-Unet. This enhancement provides valuable support for the ancillary diagnosis of mpox.

Keywords

• Mpox virus
• Swin-Unet
• expansion convolution
• triplet attention