GSANet: Enhancing accuracy in the segmentation of challenging medical images

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Medical image segmentation is crucial in the fields of computer vision and medical image processing. Its difficulty lies in precisely extracting regions of interest from images, such as blood vessels, tumors, or other anatomical structures, which is especially important in retinal images and skin pathology images. However, factors like noise, low contrast, and the complexity of target structures in medical images severely affect the accuracy of segmentation.Despite significant progress made by deep learning models such as U-Net in medical image segmentation, their performance still needs improvement when dealing with medical images with complex contexts and long-range dependencies. To tackle these challenges, we introduce a new deep learning model named GSANet.The GSANet model retains the advantages of U-Net while incorporating two innovative modules to enhance its performance in handling challenging medical image segmentation tasks. We assess the GSANet model's performance on two demanding medical image segmentation tasks, DRIVE and ISIC2018. Experimental findings illustrate that the GSANet model achieves significant performance enhancements on these tasks, surpassing the current state-of-the-art methods.The success of the GSANet model can be attributed to its effective design and implementation. GSANet excels in handling medical image segmentation tasks with complex contexts and long-range dependencies, leading to superior performance. Our research highlights the superiority of the GSANet model in medical image segmentation tasks, opening up new possibilities for further improvements in the performance and accuracy of medical image segmentation.