Skip to main content

Advertisement

SpringerLink
Log in

Glioma Tumor Grade Identification Using Artificial Intelligent Techniques

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Louis, D. N., Perry, A., Reifenberger, G., Von Deimling, A., Figarella-Branger, D., Cavenee, W. K., Ohgaki, H., Wiestler, O. D., Kleihues, P., and Ellison, D. W., World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. 131:803–820, 2016.

    Article  Google Scholar 

  2. El-Dahshan, E. A., Hosny, T., and Salem, A. B. M., Hybrid intelligent technique for MRI brain images classification. Digit. Signal Process. 20:433–441, 2010.

    Article  Google Scholar 

  3. Javed, U., Riaz, M. M., Ghafoor, A., et al., MRI brain classification using texture features, fuzzy weighting and support vector machine. Prog. Electromagn. Res. B 53:73–88, 2013.

    Article  Google Scholar 

  4. Nazir, M., and Wahid, F., A simple and intelligent approach for brain MRI classification. J. Intell. Fuzzy Syst. 28:1127–1135, 2015.

    Google Scholar 

  5. Subashini, M., Sahoo, S. K., and Sunil, V., A non-invasive methodology for the grade identification of astrocytoma using image processing and artificial intelligence techniques. Expert Syst. Appl. 43:186–196, 2016.

    Article  Google Scholar 

  6. Mohan, G., and Subashini, M. M., MRI based medical image analysis: Survey on brain tumor grade classification. Biomed. Signal Process. Control 39:139–161, 2018.

    Article  Google Scholar 

  7. Maximov, I. I., Tonoyan, A. S., and Pronin, I. N., Differentiation of glioma malignancy grade using diffusion MRI. Phys. Med. 40:24–32, 2017.

    Article  Google Scholar 

  8. Hsieha, K. L.-C., Chena, C.-Y., and Lod, C.-M., Quantitative glioma grading using transformed gray-scale invariant textures of MRI. Comput. Biol. Med. 83:102–108, 2017.

    Article  Google Scholar 

  9. Shamir, L., Orlov, N., and Eckley, D. M., Wndchrm—an open source utility for biological image analysis. Source Code Biol. Med. 3:1–13, 2008.

    Article  Google Scholar 

  10. Orlov, N., Shamir, L., Macura, T., Johnston, J., Eckley, D. M., and Goldberg, I. G., WNDCHARM: multi-purpose image classification using compound image transforms. Pattern Recognit. Lett. 29:1684–1693, 2008.

    Article  Google Scholar 

  11. Shajee Mohan, B. S., and Shanthini, K. S.: Performance analysis of classifiers with feature selection and optimization in CBIR system for biological images. In: Proceedings of the 2nd International Conference on Intelligent Systems and Image Processing, pp. 216–222, 2014.

  12. Madhukumar, S., and Santhiyakumari, N., Evaluation of k-means and fuzzy-c-means segmentation on MR images of brain. Egypt. J. Radiol. Nucl. Med. 46:475–479, 2015.

    Article  Google Scholar 

  13. Ahammed Muneer, K. V., and Paul Joseph, K., Performance analysis of combined k-means and fuzzy-c-means segmentation of MR brain images. Lect. Notes Comput. Vis. Biomech. 28:830–836, 2018.

    Article  Google Scholar 

  14. Mohsen, H., and El-Dahshan, E.-S. A., Classification using deep learning neural networks for brain tumors. Future Comput. Inf. J. 5:1–4, 2017.

    Google Scholar 

  15. Sharmaa, H., and Zerbeb, N., Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology. Comput. Med. Imaging Graph. 61:2–13, 2017.

    Article  Google Scholar 

  16. Gao, X. W., Hui, R., and Tian, Z., Classification of CT brain images based on deep learning networks. Comput. Methods Programs Biomed. 138:49–56, 2017.

    Article  Google Scholar 

  17. Vivanti, R., Joskowicz, L., Lev-Cohain, N., Ephrat, A., and Sosna, J., Patient-specific and global convolutional neural network for robust automatic liver tumor delineation in follow-up CT studies. Med. Biol. Eng. Comput. 56:1699–1713, 2018.

    Article  Google Scholar 

  18. Najafabadi, M. M., and Villanustre, F., Deep learning applications and challenges in big data analytics. J. Big Data 2:1–21, 2015.

    Article  Google Scholar 

  19. Gibson, E., Li, W., et al., NiftyNet: a deep-learning platform for medical imaging. Comput Methods Programs Biomed. 158:113–122, 2018.

    Article  Google Scholar 

  20. Talo, M., Baloglu, U. B., Yildirim, O., and Acharya, U. R., Application of deep transfer learning for automated brain abnormality classification using MR images. Cogn. Syst. Res. 54:176–188, 2019.

    Article  Google Scholar 

  21. Gudigar, A., Raghavendra, U., San, T. R., Ciaccio, E. J., and Acharya, U. R., Application of multiresolution analysis for automated detection of brain abnormality using MR images: a comparative study. Future Gener. Comput. Syst. 90:359–367, 2019.

    Article  Google Scholar 

  22. Khawaldeh, S., Pervaiz, U., et al., Noninvasive grading of glioma tumor using magnetic resonance imaging with convolutional neural networks. J. Appl. Sci. 8:1–17, 2017.

    Article  Google Scholar 

  23. Chato, L., and Latifi, S.: Machine learning and deep learning techniques to predict overall survival of brain tumor patients using MRI images. In: IEEE 17th International Conference on Bioinformatics and Bioengineering, pp. 9–14, 2017.

  24. Gladis Pushpa Rathi, V. P., and Palani, S., Brain tumor detection and classification using deep learning classifier on MRI images. J. App. Sci. Eng. Technol. 10:77–187, 2015.

    Google Scholar 

  25. Ye, C. Z., Yang, J., Geng, D. Y., Zhon, Y., and Chen, N. Y., Fuzzy rules to predict degree of malignancy in brain glioma. Med. Biol. Eng. Comput. 40:145–152, 2002.

    Article  CAS  Google Scholar 

  26. Pang, S., Du, A., Orgun, M. A., and Yu, Z.: A novel fused convolutional neural network for biomedical image classification. Med. Biol. Eng. Comput. https://doi.org/10.1007/s11517-018-1819-y, 2018

    Article  Google Scholar 

  27. Raghavendra, U., Fujita, H., Bhandary, S. V., Gudigar, A., Tan, J. H., and Acharya, U. R., Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images. Inf. Sci. 441:41–49, 2018.

    Article  Google Scholar 

  28. Tan, J. H., Bhandary, S. V., Sivaprasad, S., Hagiwara, Y., Bagchi, A., Raghavendra, U., Rao, A. K., Raju, B., Shetty, N. S., Gertych, A., Chua, K. C., and Acharya, U. R., Age-related Macular Degeneration detection using deep convolutional neural network. Future Gener. Comput. Syst. 87:127–135, 2018.

    Article  Google Scholar 

  29. Raghavendra, U., Bhat, N. S., Gudigar, A., and Acharya, U. R., Automated system for the detection of thoracolumbar fractures using a CNN architecture. Future Gener. Comput. Syst. 85:184–189, 2018.

    Article  Google Scholar 

  30. Tana, J. H., Acharyaa, U. R., Bhandaryd, S. V., Chuaa, K. C., and Sivaprasad, S., Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network. J. Comput. Sci-neth 20:70–79, 2017.

    Google Scholar 

  31. Tan, J. H., Fujita, H., Sivaprasad, S., Bhandary, S. V., Rao, A. K., Chua, K. C., and Acharya, U. R., Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network. Inf. Sci. 420:66–76, 2017.

    Article  Google Scholar 

  32. Data available at Govt: Medical College Kozhikode, India. https://www.govtmedicalcollegekozhikode.ac.in, 2017

Download references

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.

Author information

Authors and 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

Authors
  1. Ahammed Muneer K. V.
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. R. Rajendran
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paul Joseph K.
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahammed Muneer K. V..

Ethics declarations

Conflict of Interest

The authors have declared no conflict of interest.

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.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-019-1228-2

Keywords

Search

Navigation