In the context of sustainable digital transformation, healthcare is a security-critical domain that requires accurate and transparent medical image analysis. This paper presents Swin-FABNET, a cardiac MRI segmentation system that integrates a Swin Transformer backbone, UPerNet decoder, and a Fuzzy Attention (FAB) block for enhanced multi-scale feature learning. 3D cine-MRI volumes are preprocessed into 2.5D stacks with online data augmentation. Explainable AI (XAI) methods such as Grad-CAM, LIME, and SHAP are employed to visualize predictions and highlight clinically relevant cardiac regions. On the ACDC 2017 dataset, Swin-FABNET achieves an average Dice score of 0.86, outperforming the baseline and alternative designs. The results demonstrate the effectiveness of fuzzy attention in improving segmentation accuracy and contribute to the development of secure, transparent, and sustainable AI applications in healthcare.