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Related Experiment Video

Updated: Sep 13, 2025

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Histopathological-based brain tumor grading using 2D-3D multi-modal CNN-transformer combined with stacking

Naira Elazab1, Fahmi Khalifa2, Wael Gab Allah1

  • 1Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt.

Scientific Reports
|July 30, 2025
PubMed
Summary

Accurate brain tumor grading is crucial for effective treatment. This study introduces a hybrid deep learning model combining 2D-3D CNNs and Vision Transformers for superior histopathological image analysis and reliable tumor grading.

Keywords:
2D-3D convolutional neural networkBrain tumor gradingHistopathological image analysisHybrid deep learning architectureStacking classifiersVision transformer

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Area of Science:

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

Background:

  • Accurate histopathological image grading is essential for reliable brain tumor diagnosis and treatment.
  • Current methods face limitations in scalability, adaptability, and interpretability for complex spatial relationships.
  • There is a critical need for advanced approaches to enhance brain tumor grading accuracy.

Purpose of the Study:

  • To propose a comprehensive hybrid learning architecture for improved brain tumor grading.
  • To overcome the limitations of existing methods in capturing spatial and contextual information.
  • To develop a robust model for accurate classification of brain tumors from histopathological images.

Main Methods:

  • A hybrid deep learning architecture integrating 2D-3D Convolutional Neural Networks (CNNs) for hierarchical feature extraction and Vision Transformers (ViTs) for global relationship learning.
  • Complementary feature extraction techniques to capture domain-specific knowledge of tumor morphology (texture, intensity).
  • A stacking ensemble machine learning classifier combining features from CNNs and ViTs for enhanced generalization.

Main Results:

  • The proposed model achieved high performance on TCGA and DeepHisto datasets, with average accuracy, precision, and specificity of 97.1%, 97.1%, and 97.0% on TCGA, and 95%, 94%, and 95% on DeepHisto.
  • Extensive experiments, including ablation studies and cross-dataset evaluation, validated the model's effectiveness.
  • The hybrid model demonstrated significant improvements in accuracy, precision, and specificity compared to existing methods.

Conclusions:

  • The developed hybrid learning architecture effectively integrates deep learning with domain expertise for reliable brain tumor grading.
  • The model's ability to capture both local hierarchical patterns and global image relationships leads to superior diagnostic accuracy.
  • This approach offers a promising solution for accurate and scalable brain tumor grading in clinical practice.