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Glioma Tumors' Classification Using Deep-Neural-Network-Based Features with SVM Classifier.

Ghazanfar Latif1,2, Ghassen Ben Brahim2, D N F Awang Iskandar3

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Summary

This study introduces an automated method for diagnosing Glioma brain tumors using deep learning and Support Vector Machine (SVM) classification on MRI scans. The technique achieves high accuracy, improving early cancer detection and patient outcomes.

Keywords:
CNN featuresconvolutional neural networksmulti-class Glioma tumorstumor classification

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Manual diagnosis of Glioma brain tumors from Magnetic Resonance (MR) images is complex, time-consuming, and costly.
  • Early detection of Glioma is crucial for effective treatment and improved patient survival rates.
  • Automating the Glioma detection and diagnosis process from MR images is essential to overcome manual limitations.

Purpose of the Study:

  • To propose and evaluate a novel multi-class Glioma tumor classification technique.
  • To leverage deep learning for automated feature extraction from MR images.
  • To utilize a Support Vector Machine (SVM) classifier for enhanced diagnostic accuracy.

Main Methods:

  • A deep convolution neural network (CNN) was employed for automated feature extraction from multi-modal MR images.
  • Extracted deep features were subsequently fed into a Support Vector Machine (SVM) classifier for multi-class Glioma classification.
  • The proposed method was validated on the BraTS dataset, evaluating performance across four Glioma classes: Edema, Necrosis, Enhancing, and Non-enhancing.

Main Results:

  • The proposed technique achieved high classification accuracies: 96.19% for High-Grade Glioma (HGG) using FLAIR modality and 95.46% for Low-Grade Glioma (LGG) using T2 modality.
  • Performance evaluation demonstrated superior accuracy compared to existing methods reported in the literature using the same BraTS dataset.
  • The developed approach outperformed pre-trained models like GoogleNet and LeNet in classifying Glioma tumor types on the BraTS dataset.

Conclusions:

  • The proposed deep learning-based feature extraction with SVM classification offers a highly accurate and automated solution for multi-class Glioma diagnosis.
  • This automated approach has the potential to significantly reduce diagnosis time and cost, facilitating earlier detection and treatment.
  • The method's superior performance highlights its potential for clinical application in improving Glioma management and patient care.