Pancreatic cancer, a serious disease, is challenging to detect early, hindering timely treatments. Medical imaging, especially computer tomography, is vital for early diagnosis. This study focused on accurate pancreatic segmentation in medical images using subtle changes in 2D U-Net architecture to aid in diagnosis and treatment. Additionally, we proposed using a super-resolution generative adversarial network (SRGAN) for better-resolution images. The proposed methodology considering the Dice-Sørensen coefficient (DSC), showed superior DSC values: \(0.921163 \pm 0.012\) with subtle changes in U-Net architecture and \(0.5354 \pm 0.0002\) DSC with SRGAN and the proposed U-Net considering low training time. Furthermore, the recall \(0.6351 \pm 0.0159\) and precision \(0.6422 \pm 0.0056\) were obtained. These results are important to the progression of medical imaging analysis for complex decision-making.