Original scientific paper
https://doi.org/10.1080/00051144.2024.2374179
SwinVNETR: Swin V-net Transformer with non-local block for volumetric MRI Brain Tumor Segmentation
A Maria Nancy
; School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India
K. Sathyarajasekaran
; School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India
*
* Corresponding author.
Abstract
Brain Tumor Segmentation (BTS) and classification are important and growing research fields.
Magnetic resonance imaging (MRI) is commonly used in the diagnosis of brain tumours owing to
its low radiation exposure and high image quality. One of the current subjects in the field of medical imaging is how to quickly and precisely segment MRI scans of brain tumours. Unfortunately,
most existing brain tumour segmentation algorithms use inadequate 2D picture segmentation
methods and fail to capture the spatial correlation between features. In this study, we propose a segmentation model (SwinVNETR) Swin V-NetTRansformer-based architecture with a
non-local block. This model was trained using the Brain Tumor Segmentation Challenge BraTS
2021 dataset. The Dice similarity coefficients for the enhanced tumour (ET), whole tumour (WT),
and tumour core (TC) are 0.84, 0.91, and 0.87, respectively. By leveraging this methodology, we
can segment brain tumours more accurately than ever before. In conclusion, we present the
findings of our model through the application of the Grad-CAM methodology, an eXplainable
Artificial Intelligence (XAI) technique utilized to elucidate the insights derived from the model,
which helped in better understanding; doctors can better diagnose and treat patients with brain
tumours
Keywords
Deep learning; Swin Transformer; brain tumour segmentation; non-local block; explainable AI; Grad-CAM
Hrčak ID:
326331
URI
Publication date:
9.7.2024.
Visits: 0 *