Brain Tumor Segmentation in 3D MRI Images Using W-Net Architecture

Chandra Sekhar Sanaboina*
Periodicity:January - March'2025

Abstract

This research article presents an innovative 3D brain tumor segmentation method using an extended W-Net architecture, a derivative of U-Net, leveraging deep learning. Python programming on Google Colab facilitated the study, employing MRI scans from the Brats Dataset. The training dataset achieved a remarkable Dice Similarity Coefficient (DSC) and accuracy score of 0.98, showcasing the model's precision in tumor localization. The Matthews Correlation Coefficient (MCC) achieved 0.75, confirming the model's comprehensive segmentation quality. Generalization testing mirrored training outcomes, maintaining DSC and accuracy at 0.98, highlighting the model's robustness. The MCC, at 0.76, strengthened the model's ability to generalize to new data. This approach offers dependable and consistent segmentation outputs for 3-D brain MRI scans with tumor labels.

Keywords

Medical imaging, Brain tumor segmentation, U-Net model, Deep Learning, W-Net architecture, 3-D Brain MRI scans, Tumor labels, Convolution Neural Networks (CNN)

How to Cite this Article?

References

If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 15 15 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.