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Glioma Tumor Grade Identification Using Artificial Intelligent Techniques.

Ahammed Muneer K V1, V R Rajendran2, Paul Joseph K3

  • 1Department of Electrical Engineering, National Institute of Technology Calicut, 673601, Calicut, India. ahammedcet@gmail.com.

Journal of Medical Systems
|March 23, 2019
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Summary
This summary is machine-generated.

This study introduces an AI system for automatic glioma tumor grading from MRI scans. The VGG-19 deep convolutional neural network achieved 98.25% accuracy, outperforming the Wndchrm classifier.

Keywords:
Artificial intelligenceDNNGlioma gradesMRIWndchrm

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Computer-aided diagnosis (CAD) significantly enhances medical applications, particularly in detecting tumor characteristics.
  • Accurate glioma grading is crucial for effective treatment planning and patient prognosis.

Purpose of the Study:

  • To develop and evaluate an automated system for glioma tumor grade identification using magnetic resonance (MR) images.
  • To compare the performance of a Wndchrm tool-based classifier with a VGG-19 deep convolutional neural network (DNN).

Main Methods:

  • Utilized DICOM MR images for experimentation.
  • Applied preprocessing, feature extraction, and optimization techniques.
  • Employed the Wndchrm classifier with Fisher score for feature selection.
  • Implemented VGG-19 DNN with data augmentation for classification.
  • Evaluated classifier performance using accuracy, precision, sensitivity, specificity, and F1-score.

Main Results:

  • TheWndchrm classifier achieved a maximum accuracy of 92.86%.
  • The VGG-19 DNN classifier demonstrated superior performance with a maximum accuracy of 98.25%.
  • The proposed system showed better performance compared to similar recent studies.

Conclusions:

  • The VGG-19 DNN model offers a highly accurate and effective automated solution for glioma tumor grading.
  • The developed AI system holds promise for improving diagnostic efficiency and accuracy in neuro-oncology.