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Enhancing brain tumor classification through ensemble attention mechanism.

Fatih Celik1, Kemal Celik2, Ayse Celik3

  • 1Department of Geomatic Engineering, Yıldız Technical University, Esenler, Istanbul, Turkey. F.alpcelik@gmail.com.

Scientific Reports
|September 27, 2024
PubMed
Summary

This study introduces an ensemble attention mechanism for improved brain tumor detection in MRI scans. The novel method significantly enhances classification accuracy, offering a robust solution for healthcare systems.

Keywords:
AttentionBrain tumorCNNClassificationDeep learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Brain tumors represent a significant global health concern, necessitating accurate and timely detection for effective patient treatment and improved quality of life.
  • Magnetic resonance imaging (MRI) is a primary tool for brain imaging, but precise tumor identification within MRI scans is complex due to intricate anatomical variations.

Purpose of the Study:

  • To develop and evaluate an innovative ensemble attention mechanism for enhancing the accuracy of brain tumor detection in MRI images.
  • To address the challenges of precise tumor identification in MRI by leveraging multi-level feature extraction and attention mechanisms.

Main Methods:

  • The proposed approach utilizes MobileNetV3 and EfficientNetB7 to extract intermediate and final feature maps.
  • A co-attention mechanism is integrated at both intermediate and final feature map levels, followed by ensembling for enhanced feature representation.
  • This method focuses on extracting global-level features by directing attention to critical regions within the MRI data.

Main Results:

  • The ensemble attention mechanism demonstrated superior performance in detecting various feature patterns at local and global levels.
  • The system achieved high accuracy rates: 98.94% on the Figshare dataset and 98.48% on the BraTS 2019 dataset.
  • Performance metrics indicate that the proposed method surpasses existing techniques for brain tumor classification.

Conclusions:

  • The developed ensemble attention mechanism is a robust and effective tool for brain tumor detection in medical imaging.
  • This innovative approach shows significant promise for integration into healthcare systems to improve diagnostic accuracy and patient outcomes.