Skip to main content
SpringerLink
Log in

Detection and classification of glioma, meningioma, pituitary tumor, and normal in brain magnetic resonance imaging using deep learning-based hybrid model

  • Correspondence
  • Published:
Iran Journal of Computer Science Aims and scope Submit manuscript

Abstract

Pituitary, meningioma, and glioma tumors are the primary widespread brain tumors. Treatment algorithms for these tumors may differ from each other. For this reason, the detection and typing of brain tumors are necessary to determine the appropriate treatment quickly. Since imaging findings vary, even experts often have difficulties in classifying brain tumors. In this study, a convolutional neural network-based hybrid model is developed to classify glioma, meningioma, pituitary tumors, and normal brain magnetic resonance images. In the proposed hybrid model, features are obtained using pre-trained Efficientnetb0 and Shufflenet architectures. Also, the images in the existing dataset are improved, such as colored and used as the second database. With the proposed two pre-trained models, the features of individual images from two datasets are extracted. Later, these features are concatenated, and the best of these features are selected using the mRMR feature reduction method and classified using the Support Vector Machine (SVM) classifier. The proposed method achieved better results than the previously trained Efficientnetb0 and Shufflenet architectures. The accuracy value of the proposed hybrid model is 95.4%.

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
Fig. 8
Fig. 9

Data availability

The dataset used in the study is a publicly available dataset.

References

  1. Haapasalo, J., et al.: The expression of carbonic anhydrases II, IX and XII in brain tumors. Cancers 12(7), 1723 (2020)

    Article  Google Scholar 

  2. Backer-Grøndahl, T., Moen, B.H., Torp, S.H.: The histopathological spectrum of human meningiomas. Int. J. Clin. Exp. Pathol. 5(3), 231 (2012)

    Google Scholar 

  3. Ostrom, Q.T., et al.: CBTRUS statistical report: Primary brain and other central nervous system tumors diagnosed in the United States in 2013–2017. Neuro Oncol. 22(Supplement_1), iv1–iv96 (2020)

    Article  Google Scholar 

  4. Pisaneschi, M., Kapoor, G.: Imaging the sella and parasellar region. Neuroimaging Clinics 15(1), 203–219 (2005)

    Article  Google Scholar 

  5. Lengyel, E.: Ovarian cancer development and metastasis. Am. J. Pathol. 177(3), 1053–1064 (2010)

    Article  Google Scholar 

  6. Raghavendra, U., Pham, T.H., Gudigar, A., Vidhya, V., Rao, B.N., Sabut, S., Wei J.K., Ciaccio E.J., Acharya, U.R.: Novel and accurate non-linear index for the automated detection of haemorrhagic brain stroke using CT images. Complex Intell. Syst. 7, 929–940 (2021)

    Article  Google Scholar 

  7. Koh, J.E.W., et al.: Automated detection of Alzheimer’s disease using bi-directional empirical model decomposition. Pattern Recogn. Lett. 135, 106–113 (2020)

    Article  Google Scholar 

  8. Belden, C.J., et al.: Genetics of glioblastoma: a window into its imaging and histopathologic variability. Radiographics 31(6), 1717–1740 (2011)

    Article  Google Scholar 

  9. Garzín, B., et al.: Multiparametric analysis of magnetic resonance images for glioma grading and patient survival time prediction. Acta Radiol. 52(9), 1052–1060 (2011)

    Article  Google Scholar 

  10. Madlener, S., Gojo, J.: Liquid biomarkers for pediatric brain tumors: biological features, advantages and perspectives. J. Personaliz. Med. 10(4), 254 (2020)

    Article  Google Scholar 

  11. Koh, J.E.W., De Michele, S., Sudarshan, V.K., Jahmunah, V., Ciaccio, E.J., Ooi, C.P., Gururajan, R., Gururajan, R., Oh, S.L., Lewis, S.K., Green, P,H., Bhagat, G., Acharya, U.R.: Automated interpretation of biopsy images for the detection of celiac disease using a machine learning approach. Comput. Methods Programs Biomed. 203, 106010 (2021)

    Article  Google Scholar 

  12. Çinar, A., Yildirim, M.: Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture. Med. Hypotheses 139, 109684 (2020)

    Article  Google Scholar 

  13. Ghassemi, N., Shoeibi, A., Rouhani, M.: Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images. Biomed. Signal Process. Control 57, 101678 (2020)

    Article  Google Scholar 

  14. Anaraki, A.K., Ayati, M., Kazemi, F.: Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms. Biocybern. Biomed. Eng. 39(1), 63–74 (2019)

    Article  Google Scholar 

  15. Narmatha, C., Eljack, S.M., Tuka, A.A.R.M., Manimurugan, S., Mustafa, M.: A hybrid fuzzy brain-storm optimization algorithm for the classification of brain tumor MRI images. J. Ambient Intell. Humaniz. Comput. 1–9. https://doi.org/10.1007/s12652-020-02470-5 (2020)

  16. Byale, H., Lingaraju, G., Sivasubramanian, S.: Automatic segmentation and classification of brain tumor using machine learning techniques. Int. J. Appl. Eng. Res. 13(14), 11686–11692 (2018)

    Google Scholar 

  17. Bingol, H., Alatas, B.: Classification of brain tumor images using deep learning methods. Turkish J. Sci. Technol. 16(1), 137–143 (2021)

    Google Scholar 

  18. Sajjad, M., et al.: Multi-grade brain tumor classification using deep CNN with extensive data augmentation. J. Comput. Sci. 30, 174–182 (2019)

    Article  Google Scholar 

  19. Mzoughi, H., et al.: Deep multi-scale 3D convolutional neural network (CNN) for MRI gliomas brain tumor classification. J. Digit. Imaging 33, 903–915 (2020)

    Article  Google Scholar 

  20. Afshar, P., Mohammadi, A. and Plataniotis, K.N.: Brain tumor type classification via capsule networks. In 2018 25th IEEE International Conference on Image Processing (ICIP). 2018. IEEE

  21. Swati, Z.N.K., et al.: Brain tumor classification for MR images using transfer learning and fine-tuning. Comput. Med. Imaging Graph. 75, 34–46 (2019)

    Article  Google Scholar 

  22. Gumaei, A., et al.: A hybrid feature extraction method with regularized extreme learning machine for brain tumor classification. IEEE Access 7, 36266–36273 (2019)

    Article  Google Scholar 

  23. SartajBhuvaji. Brain Tumor Dataset. 2020; Available from: https://github.com/SartajBhuvaji/Brain-Tumor-Classification-DataSet/tree/master/Training.

  24. Seyyarer, E., et al.: Applications and comparisons of optimization algorithms used in convolutional neural networks. In 2019 International Artificial Intelligence and Data Processing Symposium (IDAP). 2019. IEEE

  25. Tan, M. and Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning. 2019. PMLR

  26. Zhang, X., et al.: Shufflenet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE conference on computer vision and pattern recognition. (2018)

  27. Joachims, T.: Making large-scale SVM learning practical. 1998, Technical report

  28. Moorthy, U., Gandhi, U.D.: Forest optimization algorithm-based feature selection using classifier ensemble. Comput. Intell. 36(4), 1445–1462 (2020)

    Article  MathSciNet  Google Scholar 

  29. Pham, T.H., Sree, V., Mapes, J., Dua, S., Lih, O.S., Koh, J.E., Ciaccio, E.J., Acharya, U.R.: A novel machine learning framework for automated detection of arrhythmias in ECG segments. J. Ambient Intell. Hum. Comput. 1–18 (2021). https://doi.org/10.1007/s12652-020-02779-1

  30. Toğaçar, M., et al.: A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models. Irbm 41(4), 212–222 (2020)

    Article  Google Scholar 

  31. Gonzalez-Lopez, J., Ventura, S., Cano, A.: Distributed multi-label feature selection using individual mutual information measures. Knowl.-Based Syst. 188, 105052 (2020)

    Article  Google Scholar 

  32. Oh, S.L., et al.: Classification of heart sound signals using a novel deep wavenet model. Comput. Methods Programs Biomed. 196, 105604 (2020)

    Article  Google Scholar 

  33. Eroğlu, Y., Yildirim, M., Çinar, A.: Convolutional Neural Networks Based classification of breast ultrasonography images by hybrid method with respect to benign, malignant, and normal using mRMR. Comput. Biol. Med. 133, 104407 (2021)

    Article  Google Scholar 

  34. Çınar, A., Yıldırım, M., Eroğlu, Y.: Classification of pneumonia cell images using improved ResNet50 model. Traitement du Signal 38(1), 165–173 (2021)

    Article  Google Scholar 

  35. Cengil, E. and Cinar, A.: Classification of human driving behaviour images using convolutional neural network architecture. In International Conference on Hybrid Artificial Intelligence Systems. 2019. Springer

  36. Yildirim, O., et al.: Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records. Comput. Methods Programs Biomed. 197, 105740 (2020)

    Article  Google Scholar 

  37. Juneja, B., et al.: Varied presentation of skin adnexal tumors-A case series with review of literature. Stem Cells 2, 3 (2020)

    Google Scholar 

  38. Zeng, Q., et al.: Noninvasive evaluation of cerebral glioma grade by using multivoxel 3D proton MR spectroscopy. Magn. Reson. Imaging 29(1), 25–31 (2011)

    Article  Google Scholar 

  39. Kalpathy-Cramer, J., et al.: Advanced magnetic resonance imaging of the physical processes in human glioblastoma. Can. Res. 74(17), 4622–4637 (2014)

    Article  Google Scholar 

  40. Galldiks, N., et al.: Molecular imaging and advanced MRI findings following immunotherapy in patients with brain tumors. Expert Rev. Anticancer Ther. 20(1), 9–15 (2020)

    Article  Google Scholar 

Download references

Acknowledgements

Thanks to the dataset owners for sharing the brain MRI images used in this study [23].

Funding

Received no funding to support our study.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Malatya Turgut Ozal University, Malatya, Turkey

    Muhammed Yildirim

  2. Department of Computer Engineering, Bitlis Eren University, Bitlis, Turkey

    Emine Cengil

  3. Department of Radiology, Firat University School of Medicine, Elazig, Turkey

    Yeşim Eroglu

  4. Department of Computer Engineering, Firat University, Elazig, Turkey

    Ahmet Cinar

Authors
  1. Muhammed Yildirim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emine Cengil
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yeşim Eroglu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ahmet Cinar
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The authors contributed equally to the paper.

Corresponding author

Correspondence to Muhammed Yildirim.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interest in the study.

Consent statement/ethical approval

Not required.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yildirim, M., Cengil, E., Eroglu, Y. et al. Detection and classification of glioma, meningioma, pituitary tumor, and normal in brain magnetic resonance imaging using deep learning-based hybrid model. Iran J Comput Sci 6, 455–464 (2023). https://doi.org/10.1007/s42044-023-00139-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42044-023-00139-8

Keywords

Search

Navigation