Advancing Brain Tumor MRI Segmentation

Year : 2025 | Volume : 12 | Issue : 02 | Page : 28-33
    By

    Utkarsh Kant Mishra,

  • Anand Prakash Yadav,

  • Satyam Kumar,

  1. Student, Department of Computer Science and Engineering (Artificial Intelligence), Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh, India
  2. Student, Department of Computer Science and Engineering (Artificial Intelligence), Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh, India
  3. Student, Department of Computer Science and Engineering (Artificial Intelligence), Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh, India

Abstract

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Segmentation of brain tumors in MRI scans is an integral part of neuroimaging carried out for diagnostic and therapeutic interventions. Given that manual segmentation is cumbersome and highly variable, there arises a need for automated, more precise segmentation solutions. This project, ‘Machine Learning and Deep Neural Networks to Advance Brain Tumor MRI Segmentation’ will develop a better, efficient, and accurate segmentation model to help clinicians identify brain tumors with greater accuracy. The most commonly used imaging technique applied in the evaluation of brain tumors is magnetic resonance imaging, which allows radiologists to view the interior of the brain using radio waves and magnets. However, it is time-consuming and complex to differentiate between tumorous and nontumorous regions due to the complexity of the region involved with the tumor. Therefore, automatic reliable segmentation and predictability are required in brain tumor segmentation. This gives us a trustworthy and efficient variant of neural network-based segmentation, which we propose to incorporate attention into convolutional neural networks for brain tumor segmentation. The encoder component of UNET’s pre-trained VGG19 network induces noise in segmentation through adjacent decoder components with attention gates while employing a denoising mechanism to counter overfitting. Our address here in Segmentation is with the dataset from BRATS’20 containing four different MRI modalities along with one target mask file. For the tumors identified in enhancing core and whole parts, respectively, the algorithm described above yielded a dice similarity coefficient equal to 0.83, 0.86, and 0.90.

Keywords: Magnetic resonance imaging (MRI), convolutional neural network (CNN), attention mechanism, deep learning, embedded medical systems, electronic health diagnostics

[This article belongs to Recent Trends in Electronics Communication Systems ]

How to cite this article:
Utkarsh Kant Mishra, Anand Prakash Yadav, Satyam Kumar. Advancing Brain Tumor MRI Segmentation. Recent Trends in Electronics Communication Systems. 2025; 12(02):28-33.
How to cite this URL:
Utkarsh Kant Mishra, Anand Prakash Yadav, Satyam Kumar. Advancing Brain Tumor MRI Segmentation. Recent Trends in Electronics Communication Systems. 2025; 12(02):28-33. Available from: https://journals.stmjournals.com/rtecs/article=2025/view=0


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Regular Issue Subscription Review Article
Volume 12
Issue 02
Received 08/05/2025
Accepted 09/05/2025
Published 09/06/2025
Publication Time 32 Days
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