Accurate segmentation in medical imaging, particularly for modalities such as Chest X-rays, CT scans, and microscopic images, is critical for diagnosis and treatment. However, noisy and low-quality data can significantly affect performance. This paper presents a novel framework that integrates Noise2Split denoising with a Hybrid Swin Transformer U-Net to enhance segmentation accuracy in these challenging medical imaging tasks. By combining Noise2Split's effective noise reduction with the Swin Transformer's advanced feature extraction and U-Net's robust segmentation architecture, the model efficiently addresses both noise and segmentation challenges. The Swin Transformer effectively captures both local and global context, while the skip connections in U-Net contribute to recovering detailed high- resolution features. Extensive experiments on Chest X-rays, CT scans, and microscopic images demonstrate that this integrated model performs better than traditional methods in terms of segmentation accuracy, making it a valuable tool for clinical applications where imaging quality is compromised.