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Automated multi-class MRI brain tumor classification and segmentation using deformable attention and saliency

Erfan Zarenia1,2, Amirhossein Akhlaghi Far3, Khosro Rezaee4

  • 1Department of Radiology and Nuclear Medicine, School of Allied Medical Sciences, Kermanshah University of Medical Sciences, Kermanshah, Iran.

Scientific Reports
|March 8, 2025
PubMed
Summary

This study introduces a novel automated method for brain tumor classification and segmentation using enhanced Magnetic Resonance (MR) imaging analysis. The new model significantly improves diagnostic accuracy, aiding in earlier and more effective patient treatment.

Keywords:
Attention mechanismBrain tumorsDeep learningDeformable modelMagnetic resonance imagingSaliency map

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Automatic classification and segmentation of medical images are crucial for timely brain tumor diagnosis and treatment.
  • Magnetic Resonance (MR) imaging is the standard for brain tumor diagnostics but is labor-intensive.
  • Automated methods are essential to improve efficiency and accuracy in clinical settings.

Purpose of the Study:

  • To develop a novel framework for automated brain tumor classification and segmentation.
  • To enhance diagnostic accuracy and efficiency in brain tumor detection.
  • To enable classification of multiple tumor types and extraction of diagnostic features.

Main Methods:

  • Applied data augmentation techniques to MR images.
  • Developed a hierarchical multiscale deformable attention module (MS-DAM) to capture complex tumor patterns.
  • Conducted a comprehensive segmentation process on a large dataset of 14 tumor types.

Main Results:

  • The MS-DAM model demonstrated superior accuracy in capturing irregular tumor patterns compared to existing attention modules.
  • Achieved over 96.5% accuracy in classifying multiple brain tumor types from a diverse Kaggle dataset.
  • Validated the model's efficacy as a decision support system for diagnostic imaging.

Conclusions:

  • The proposed framework offers a highly promising approach for automated brain tumor classification and segmentation.
  • Significant advancements in diagnostic imaging clinics are expected.
  • Paves the way for more efficient, accurate, and scalable tumor detection methodologies.