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Updated: Feb 28, 2026

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SCAG-Net: Automated Brain Tumor Prediction from MRI Using Cuttlefish-Optimized Attention-Based Graph Networks.

Vijay Govindarajan1, Ashit Kumar Dutta2,3, Amr Yousef4,5

  • 1Distribution and Supply Technology, Expedia Group, Seattle, WA 98119, USA.

Diagnostics (Basel, Switzerland)
|February 27, 2026
PubMed
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This summary is machine-generated.

A novel SCAG-Net approach enhances brain tumor recognition from MRI scans, improving accuracy and efficiency for faster diagnoses. This automated system overcomes challenges like tumor variability and infiltrative gliomas.

Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in oncology
  • Computational neuroscience

Background:

  • Automated systems are crucial for accurate and timely brain tumor recognition, reducing diagnostic delays and human error.
  • High variability in tumor characteristics (location, size, shape) and infiltrative nature of gliomas pose significant segmentation and identification challenges.
  • Existing automated systems struggle with complex tumor features and heterogeneity, impacting diagnostic efficiency.

Purpose of the Study:

  • To develop an advanced automated system for improved brain tumor recognition from MRI images.
  • To address the complexities of tumor variability, infiltrative gliomas, and feature redundancy.
  • To enhance diagnostic efficiency and accuracy in clinical decision-making.

Main Methods:

Keywords:
MRISwin-UNetattention graph neural networksbrain tumorcuttlefishredundant featuressegmentation

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  • Integration of Swin-UNet for initial image region identification and error minimization.
  • Application of a cuttlefish-optimized attention-based Graph Neural Network (SCAG-Net) for feature exploration and selection.
  • Utilizing attention graph networks to process structural and heterogeneous information for robust classification.

Main Results:

  • The SCAG-Net approach achieved high recognition accuracy on public datasets (BRATS 2018-2020, Figshare).
  • Key performance metrics include a Dice coefficient of 0.989, Intersection over Union of 0.969, and classification accuracy of 0.992.
  • The proposed system demonstrated statistically significant improvements over recent benchmark models (p < 0.05).

Conclusions:

  • SCAG-Net offers a robust, efficient, and clinically deployable framework for brain tumor recognition from MRI.
  • The approach effectively handles tumor heterogeneity and infiltrative gliomas, crucial for accurate diagnosis.
  • This method supports rapid and precise diagnosis, maintaining expert-level performance in medical applications.