| Summary: | The computer-aided diagnosis (CAD) of skin diseases relies heavily on automated skin lesion segmentation, albeit presenting considerable challenges due to lesion diversity in shape, size, color, and texture, as well as potential blurry boundaries with surrounding tissues. Traditional Convolutional Neural Networks (CNN) typically underperform in this domain, given their inherent constraints in global context information capture. In the present study, we present a new U-shaped network, Dual-Encoder U-Net (DEU-Net), which is based on an encoder-decoder architecture. DEU-Net integrates a dual-encoder branch comprising a convolutional encoder and a transformer encoder, thereby facilitating the concurrent extraction of local features and global contextual information. Additionally, in order to enhance the performance of DEU-Net, we employ an integrated test-time augmentation technique. To ascertain the efficiency and superiority of our proposed methodology, we performed comprehensive experiments across four widely accessible skin lesion datasets, namely ISIC 2016, ISIC 2017, ISIC 2018, and PH2. The Dice coefficients achieved on these datasets were 92.90%, 87.16%, 90.81%, and 95.65%, respectively. These results demonstrate superior performance compared to most current state-of-the-art methods. The source code is released at <uri>https://github.com/alikm6/DEU-Net</uri>.
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