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

Chandra Sekhar Sanaboina*
Department of Computer Science and Engineering, Jawaharlal Nehru Technological University Kakinada (JNTUK), Andhra Pradesh, India.
Periodicity:January - March'2025
DOI : https://doi.org/10.26634/jfet.20.2.21656

Abstract

Segmenting the 3D MRI images by the computer to identify the brain tumors is a very critical and challenging task till the invention of the deep learning algorithms. The previous works used some traditional methods like Mathematical Morphological Reconstruction (MMR), superpixel-level features extracted from 3D volumetric MR images, ensemble approaches, CNN, U-Net, etc., to achieve enhanced accuracy in segmenting different tumor regions. This study 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, Deep Learning, W-Net Architecture, 3D MRI Brain Scans, Convolutional Neural Networks (CNN), BraTS Dataset.

How to Cite this Article?

Sanaboina, C. S. (2025). Brain Tumor Segmentation in 3D MRI Images using W-Net Architecture. i-manager’s Journal on Future Engineering & Technology, 20(2), 33-43. https://doi.org/10.26634/jfet.20.2.21656

References

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