DualA-Net: A generalizable and adaptive network with dual-branch encoder for medical image segmentation.

• Improved cost-effective U-type deep learning model for medical image segmentation. • Dual-branch encoder enhance the extraction of semantic information from input images. • Mutations facilitates more flexible and comprehensive feature fusion. • Generalizable model with fewer parameters, lower computational complexity. Medical image segmentation is a critical task in early disease detection and diagnosis. In recent years, numerous variants of U-Net and Transformer-based models have demonstrated success in medical image segmentation. But these models still have certain limitations, particularly regarding the extraction of semantic information and high computational complexity. In order to tackle these challenges, we introduce a cutting-edge model called DualA-Net, which incorporates dual-branch encoder, multi-scale skip connections and Adaptive Receptive Field Selection Decoder(ARFSD). These innovations we proposed enable the model to intelligently adapt to and focus on relevant areas, ensuring its adaptability and thus improving the accuracy and efficiency of the segmentation process. To assess the performance of DualA-Net, its generalization capability was evaluated on five datasets of different segmentation tasks. The experimental results showed that the DualA-Net model performed the best on these datasets. Moreover, it minimized the parameter count and computational complexity. These findings provide evidence supporting the versatility and effectiveness of DualA-Net in medical image segmentation. Codes are available at https://github.com/Ziii1/DualA-Net. [ABSTRACT FROM AUTHOR]