  The segmentation of medical images is important for the improvement and
creation of healthcare systems, particularly for early disease detection and
treatment planning. In recent years, the use of convolutional neural networks
(CNNs) and other state-of-the-art methods has greatly advanced medical image
segmentation. However, CNNs have been found to struggle with learning
long-range dependencies and capturing global context due to the limitations of
convolution operations. In this paper, we explore the use of transformers and
CNNs for medical image segmentation and propose a hybrid architecture that
combines the ability of transformers to capture global dependencies with the
ability of CNNs to capture low-level spatial details. We compare various
architectures and configurations and conduct multiple experiments to evaluate
their effectiveness.
