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  11. An Effective Flowchart For Multimodal Brain Tumor Binary Classification With Ranked 3d Texture Features

An effective flowchart for multimodal brain tumor binary classification with ranked 3D texture features

Mücahid Barstuğan1

  • 1Department of Electrical and Electronics Engineering, Faculty of Engineering and Natural Sciences, Konya Technical University, Konya, Turkey. mbarstugan@ktun.edu.tr.

Scientific Reports
|August 20, 2025

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View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a 3D feature extraction method for improved brain tumor classification. The novel approach enhances accuracy in differentiating high-grade and low-grade gliomas using machine learning.

Area of Science:

  • Medical Imaging
  • Machine Learning
  • Computational Biology

Background:

  • Brain tumor classification is complex due to variations in shape, density, and size.
  • Classical 2D texture features offer limited accuracy for brain tumor analysis.
  • 3D magnetic resonance imaging (MRI) provides richer data for tumor characterization.

Purpose of the Study:

  • To develop and evaluate a novel 3D feature extraction and ranking framework for accurate brain tumor classification.
  • To compare the performance of various feature sets and ranking methods in differentiating high-grade and low-grade gliomas.
  • To assess the efficacy of machine learning classifiers, including Support Vector Machines (SVM), in brain tumor diagnosis.

Main Methods:

  • Utilized 3D MRI data from the BraTS 2017 dataset for high-grade and low-grade glioma classification.
Keywords:
3D feature extractionBinary classificationBrain tumorFeature ranking

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  • Extracted 3D texture features from FLAIR, T1, T1-contrast enhanced (T1c), and T2 MRI phases using a 3D gray level co-occurrence matrix.
  • Applied feature ranking methods (Bhattacharyya, entropy, ROC, t-test, Wilcoxon) and classified features using gradient boosting, SVM, and random forest algorithms.
  • Evaluated performance using sensitivity, specificity, accuracy, precision, and F-score via cross-validation (2-fold, 5-fold, 10-fold).

    • Main Results:

    • The combination of all four MRI phases (FLAIR+T1+T1c+T2) with the entropy feature-ranking method and 2-fold cross-validation yielded the most effective classification scheme.
    • The proposed machine learning framework achieved high performance metrics: 100% sensitivity, 97.29% specificity, 99.30% accuracy, 99.07% precision, and 99.53% F-score for glioma classification using SVM.
    • The 3D feature extraction approach significantly outperformed traditional 2D methods.

    Conclusions:

    • 3D feature extraction and ranking methods overcome the limitations of 2D texture analysis for brain tumor classification.
    • The developed machine learning-based framework offers a robust and highly accurate system for glioma grading.
    • This novel approach demonstrates competitive performance compared to existing state-of-the-art methods in brain tumor classification.