Skip to main content
SpringerLink
Log in

Political exponential deer hunting optimization-based deep learning for brain tumor classification using MRI

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

In this work, three deep learning classifiers, namely pyramid scene parsing network (PSPNet), Shepard convolutional neural network (ShCNN) and deep convolutional neural network (Deep CNN), are introduced to segment, detect and classify the tumor region from magnetic resonance images (MRI). The pre-processing is carried out by exploiting region of interest extraction that removes the irrelevant noise that exists in the images. The PSPNet segments the tumor region from the MRI to progress the efficacy of classification. The ShCNN detects the segmented image as either normal or abnormal image. Moreover, the tumor classification is completed using Deep CNN, which classifies the detected image as either benign, edema, core or malignant. In addition, the training process of three classifiers is performed by exploiting the adopted political exponential deer hunting optimization algorithm, which is designed by incorporating political optimizer, and exponential deer hunting optimization. Moreover, the performance analysis proved that the adopted tumor detection method obtained the optimal performance based on the testing accuracy, sensitivity, as well as specificity of 0.904, 0.919 and 0.899, and the developed classification approach obtained maximum testing accuracy, sensitivity, as well as specificity of 0.929, 0.935 and 0.902, respectively.

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

Similar content being viewed by others

References

  1. Mallick, P.K., Ryu, S.H., Satapathy, S.K., Mishra, S., Nguyen, G.N., Tiwari, P.: Brain MRI image classification for cancer detection using deep wavelet autoencoder-based deep neural network. IEEE Access 7, 46278–46287 (2019)

    Article  Google Scholar 

  2. Abraham, B., Nair, M.S.: Computer-aided diagnosis of clinically significant prostate cancer from MRI images using sparse autoencoder and random forest classifier. Biocybern. Biomed. Eng. 38(3), 733–744 (2018)

    Article  Google Scholar 

  3. Bhambere, H.S.S., Abhishek, B., Sumit, H.: Rapid digitization of healthcare: a review of COVID-19 impact on our health systems. Int. J. All Res. Educ. Sci. Methods 9, 1457–1459 (2021)

    Google Scholar 

  4. Anitha, V., Murugavalli, S.J.I.C.V.: Brain tumour classification using two-tier classifier with adaptive segmentation technique. IET Comput. Vis. 10(1), 9–17 (2016)

    Article  Google Scholar 

  5. Polepaka, S., Rao, C.S., Mohan, M.C.: IDSS-based two stage classification of brain tumor using SVM. Heal. Technol. 10(1), 249–258 (2020)

    Article  Google Scholar 

  6. Yadav, P.: Case retrieval algorithm using similarity measure and fractional brain storm optimization for health informaticians. Int. Arab J. Inf. Technol. (IAJIT) 16(2), 281–287 (2019)

    Google Scholar 

  7. Wadhwa, A., Bhardwaj, A., Verma, V.S.: A review on brain tumor segmentation of MRI images. Magn. Reson. Imaging 61, 247–259 (2019)

    Article  Google Scholar 

  8. Ahmadvand, A., Daliri, M.R., Zahiri, S.M.: Segmentation of brain MR images using a proper combination of DCS based method with MRF. Multimed. Tools Appl. 77(7), 8001–8018 (2018)

    Article  Google Scholar 

  9. Arasi, P.R.E., Suganthi, M.: A clinical support system for brain tumor classification using soft computing techniques. J. Med. Syst. 43(5), 1–11 (2019)

    Article  Google Scholar 

  10. Rupapara, V., Narra, M., Gonda, N.K., Thipparthy, K., Gandhi, S.: Auto-encoders for content-based image retrieval with its implementation using handwritten dataset. In: Proceedings of 2020 5th International Conference on Communication and Electronics Systems (ICCES), pp. 289–294 (2020)

  11. Gokulkumari, G.: Classification of brain tumor using manta ray foraging optimization-based DeepCNN classifier. Multimed. Res. 3, 4 (2020)

    Article  Google Scholar 

  12. Raju, A.R., Suresh, P., Rao, R.R.: Bayesian HCS-based multi-SVNN: a classification approach for brain tumor segmentation and classification using Bayesian fuzzy clustering. Biocybern. Biomed. Eng. 38(3), 646–660 (2018)

    Article  Google Scholar 

  13. Gopal, A.: Hybrid classifier: brain tumor classification and segmentation using genetic-based grey wolf optimization. Multimed. Res. 3, 2 (2020)

    Google Scholar 

  14. Cristin, D.R., Kumar, D.K.S., Anbhazhagan, D.P.: Severity level classification of brain tumor based on MRI images using fractional-chicken swarm optimization algorithm. Comput. J. (2021)

  15. Amin, J., Sharif, M., Gul, N., Yasmin, M., Shad, S.A.: Brain tumor classification based on DWT fusion of MRI sequences using convolutional neural network. Pattern Recogn. Lett. 129, 115–122 (2020)

    Article  Google Scholar 

  16. Swati, Z.N.K., Zhao, Q., Kabir, M., Ali, F., Ali, Z., Ahmed, S., Lu, J.: Brain tumor classification for MR images using transfer learning and fine-tuning. Comput. Med. Imaging Graph. 75, 34–46 (2019)

    Article  Google Scholar 

  17. 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 

  18. Kumar, S., Mankame, D.P.: Optimization driven deep convolution neural network for brain tumor classification. Biocybernet. Biomed. Eng. 40(3), 1190–1204 (2020)

    Article  Google Scholar 

  19. Amin, J., Sharif, M., Yasmin, M., Fernandes, S.L.: A distinctive approach in brain tumor detection and classification using MRI. Pattern Recogn. Lett. 139, 118–127 (2020)

    Article  Google Scholar 

  20. Amin, J., Sharif, M., Gul, N., Raza, M., Anjum, M.A., Nisar, M.W., Bukhari, S.A.C.: Brain tumor detection by using stacked autoencoders in deep learning. J. Med. Syst. 44(2), 1–12 (2020)

    Article  Google Scholar 

  21. Askari, Q., Younas, I., Saeed, M.: Political optimizer: a novel socio-inspired meta-heuristic for global optimization. Knowl. Based Syst. 195, 105709 (2020)

    Article  Google Scholar 

  22. Saccucci, M.S., Amin, R.W., Lucas, J.M.: Exponentially weighted moving average control schemes with variable sampling intervals. Commun. Stat. Simul. Computat. 21(3), 627–657 (1992)

    Article  MathSciNet  Google Scholar 

  23. Brammya, G., Praveena, S., NinuPreetha, N.S., Ramya, R., Rajakumar, B.R., Binu, D.: Deer hunting optimization algorithm: a new nature-inspired meta-heuristic paradigm. Comput. J. (2019)

  24. BRATS 2018 database. https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=37224922. Accessed on Oct, 2021

  25. Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2881–2890 (2017)

  26. Ren, J.S., Xu, L., Yan, Q., Sun, W.: Shepard convolutional neural networks. Adv. Neural. Inf. Process. Syst. 28, 901–909 (2015)

    Google Scholar 

  27. Sugave, S., Jagdale, B.: Monarch-EWA: monarch-earthworm-based secure routing protocol in IoT. Comput. J. 63(6), 817–831 (2020)

    Article  Google Scholar 

  28. Figshare dataset taken from. https://figshare.com/articles/brain_tumor_dataset/1512427. Accessed on Oct, 2021

  29. Ghanavati, S., Li, J., Liu, T., Babyn, P.S., Doda, W., Lampropoulos, G.: Automatic brain tumor detection in magnetic resonance images. In: 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp. 574–577 (2012)

Download references

Author information

Authors and Affiliations

  1. Department of ECE, Panimalar Engineering College, Chennai, India

    S. Rajakumar

  2. Department of EEE, New Horizon College of Engineering (Autonomous), Bengaluru, Karnataka, 560103, India

    V. Agalya

  3. Department of Electronics and Communication Engineering, Rajalakshmi Institute of Technology, Chennai, India

    R. Rajeswari

  4. Department of Computer Science and Engineering, School of Computing, MIT Art, Design and Technology University, Pune, Maharastra, India

    Rohit Pachlor

Authors
  1. S. Rajakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. Agalya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. Rajeswari
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rohit Pachlor
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Rajakumar.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rajakumar, S., Agalya, V., Rajeswari, R. et al. Political exponential deer hunting optimization-based deep learning for brain tumor classification using MRI. SIViP 17, 3451–3459 (2023). https://doi.org/10.1007/s11760-023-02567-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-023-02567-2

Keywords

Search

Navigation