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Glioma Tumor Grade Identification Using Artificial Intelligent Techniques

Journal of Medical Systems volume 43, Article number: 113 (2019) Cite this article

Abstract

Computer aided diagnosis using artificial intelligent techniques made tremendous improvement in medical applications especially for easy detection of tumor area, tumor type and grades. This paper presents automatic glioma tumor grade identification from magnetic resonant images using Wndchrm tool based classifier (Weighted Neighbour Distance using Compound Heirarchy of Algorithms Representing Morphology) and VGG-19 deep convolutional neural network (DNN). For experimentation, DICOM images are collected from reputed government hospital and the proposed intelligent system categorized the tumor into four grades such as low grade glioma, oligodendroglioma, anaplastic glioma and glioblastoma multiform. After preprocessing, features are extracted, optimized and then classified using Windchrm tool where the most significant features are selected on the basis of Fisher score. In the case of DNN classifier, data augmentation is also performed before applying the images into the deep learning network. The performance of the classifiers are analysed with various measures such as accuracy, precision, sensitivity, specificity and F1-score. The results showed reasonably good performance with a maximum classification accuracy of 92.86% for the Wndchrm classifier and 98.25% for VGG-19 DNN classifier. The results are also compared with similar recent works and the proposed system is found to have better performance.

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Acknowledgments

We are grateful to the principal and all the faculty in radiology department of government medical college Kozhikode, India for providing MR images and giving active support to carry out our research work.

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Affiliations

  1. Department of Electrical Engineering, National Institute of Technology Calicut, 673601, Calicut, India

    Ahammed Muneer K. V. & Paul Joseph K.

  2. Department of Radiology, Government Medical College Kozhikode, 673008, Calicut, India

    V. R. Rajendran

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  1. Ahammed Muneer K. V.
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Correspondence to Ahammed Muneer K. V..

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Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors. The data only is collected from the government medical college, Kozhikode, India and is in accordance with the ethical standards of the institution.

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This article is part of the Topical Collection on Image & Signal Processing

Appendix: Performance metrics

Appendix: Performance metrics

In order to measure the optimality of the proposed systems, various performance indices are used. The various measures calculated are accuracy, sensitivity and specificity, precision and F1-score. All the parameters are derived from confusion matrix. The parameters are evaluated using the following values:

True Positive (TP)::

abnormal brain accurately recognized as abnormal.

True Negative (TN)::

normal brain accurately recognized as normal.

False Positive (FP)::

normal brain inaccurately recognized as abnormal.

False Negative (FN)::

abnormal brain inaccurately recognized as normal.

$$ \begin{array}{@{}rcl@{}} Accuracy &=& \frac{TP+TN}{TP+TN+FP+FN}\\ Precision &= &\frac{TP}{TP+FP}\\ Recall(Sensitivity) &=& \frac{TP}{TP+FN}\\ Specificity &=& \frac{TN}{TN+FP}\\ F1-Score &=& \frac{2*Precision*Recall}{Precision+Recall} \end{array} $$

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Ahammed Muneer K. V., Rajendran, V.R. & K., P.J. Glioma Tumor Grade Identification Using Artificial Intelligent Techniques. J Med Syst 43, 113 (2019). https://doi.org/10.1007/s10916-019-1228-2

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Keywords

  • MRI
  • Artificial intelligence
  • Wndchrm
  • DNN
  • Glioma grades