MedSwin: A Novel Swin Transformer based Approach for Precise Renal Cancer Segmentation from 3D CT Scan Images

In the process of treatment planning and early diagnosis, precise detection and segmentation of specific region is crucial in case of renal cancer infected patients. In complex 3D CT scan images, traditional CNNs fail in terms of long-range dependency capture and segmentation difficulties where transformer-based embeddings can lead to optimal renal cancer segmentation. This paper introduces one such concept called "MedSwin", a Swin transformer-based framework for segmenting 3D CT scan images and detecting renal cancer. Leveraged by Swin Tra8nsformer module, the framework can capture both local and global spatial relationships in volumetric medical images. Additionally, the model is infused with self-attention mechanism and shifted window partitioning for superior feature representation. The experimental evaluations on standard benchmark renal cancer dataset like KiTS23 demonstrate the effectiveness of model with an average Dice Similarity Co-efficient (DSC) of 93.5%, Mean Intersection Over Union (mIoU) of 90.2%, and high inference efficiency of 16.7 FPS which surpasses other traditional segmentation approaches like U-Net and Swin-Unet. This proposed model represents a major advancement in AI-driven medical image analysis helping in reliable and accurate renal cancer diagnosis.