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Related Experiment Video

Updated: Aug 23, 2025

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions
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Multiclass tumor identification using combined texture and statistical features.

Ghazanfar Latif1,2, Abul Bashar3, D N F Awang Iskandar4

  • 1Computer Science Department, Prince Mohammad Bin Fahd University, Al Khobar, Saudi Arabia. glatif@pmu.edu.sa.

Medical & Biological Engineering & Computing
|November 3, 2022
PubMed
Summary

This study introduces an automated system for glioma brain tumor classification using combined texture and statistical features. The random forest classifier achieved high accuracy, outperforming existing methods for early cancer detection.

Keywords:
Glioma tumorMagnetic resonance imagingMulticlass tumor classificationStatistical featuresTexture featuresTumor detection

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Early brain tumor detection is crucial for effective treatment and improved patient outcomes.
  • Current manual diagnosis from MR images is challenging, time-consuming, and prone to human error.
  • Differentiating glioma subtypes based on imaging features can be difficult due to similarities in density, size, and shape.

Purpose of the Study:

  • To develop an automated system for classifying glioma brain tumors into four types: necrosis, edema, enhancing, and non-enhancing.
  • To improve the accuracy and efficiency of brain tumor diagnosis.
  • To compare the performance of different machine learning classifiers for this task.

Main Methods:

  • A novel approach combining texture features from Discrete Wavelet Transform (DWT) and first- and second-order statistical features was proposed.
  • Four machine learning classifiers were evaluated: Support Vector Machines (SVM), Random Forest (RF), Multilayer Perceptron (MLP), and Naïve Bayes (NB).
  • Experiments were conducted using the BraTS 2018 dataset.

Main Results:

  • The Random Forest (RF) classifier achieved the highest average accuracy when using the combined DWT and statistical features.
  • The highest average accuracies reported were 89.59% for High-Grade Glioma (HGG) and 90.28% for Low-Grade Glioma (LGG).
  • The proposed method demonstrated superior performance compared to existing literature methods.

Conclusions:

  • The developed automated system effectively classifies glioma brain tumors.
  • Combining DWT texture and statistical features with the Random Forest classifier offers a robust approach for accurate brain tumor diagnosis.
  • This automated method has the potential to reduce diagnostic errors and improve patient management.