Abstract:
One of the most recent deep learning innovations is the use of hybrids of
transformers-CNN architectures for medical image segmentation. Given that
computer vision and medical imaging share certain similarities, it is only logical to
wonder whether the transformer hybrids can change the way that medical imaging is
done. Brain tumor segmentation, especially one of its subregions i.e., Enhancing
Tumor, is quite challenging to segment. In his paper, we have used a modified
version of the SWIN UNETR model, CT Hybrid. We have done transfer learning and
freezing some of the model’ s layers while tuning some hyperparameters, we have
achieved SOTA performance within a few epochs, utilizing minimal time and
computational resources. We retrained the CT model using the BraTS 2023 dataset.
For enhancing tumor our score showed improvement and outperformed recent best performing UNet and transformer architectures.
