SpringerLink
Log in

An optimized eagle adaboost model for brain tumor classification and severity analysis system

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

An early diagnosis system is needed to reduce the death rate due to brain tumor disease. Hence, the imaging system is introduced for visualizing the tumor region from the brain MRI images. But, the tumor features are not the same for all cases, resulting in poor tracking and segmentation outcomes. Henceforth, some neural approach functional process is insufficient for tracking and segmenting the tumor region. To end these issues, the presented article has focused on develo** a novel Optimized ensemble learning model known as the Optimized Eagle Adaboost Mechanism (OEAM) to track the tumor region efficiently. Once the tumor is identified, then the segmentation process is done. Finally, the predicted tumor type and severity stages were classified with the help of Eagle Fitness. Moreover, the designed novel approach is validated in the MATLAB environment. The robustness was valued based on segmentation accuracy, precision, recall, F-measure, error rate, and execution time. Moreover, the presented Model has 99% accuracy for segmenting the tumor region.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Algorithm 1:
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Muhammad K, Khan S, Ser JD, Albuquerque VHCD (2020) Deep learning for multigrade brain tumor classification in smart healthcare systems: A prospective survey. IEEE Trans Neural Netw Learn Syst 32(2):507–522. https://doi.org/10.1109/TNNLS.2020.2995800

    Article  Google Scholar 

  2. Amin J, Sharif M, Gul N, Yasmin M, Shad SA (2020) Brain tumor classification based on DWT fusion of MRI sequences using convolutional neural network. Pattern Recognit Lett 129:115–122. https://doi.org/10.1016/j.patrec.2019.11.016

    Article  Google Scholar 

  3. Mittal M, Goyal LM, Kaur S, Kaur I, Verma A, Hemanth DJ (2019) Deep learning based enhanced tumor segmentation approach for MR brain images. Appl Soft Comput 78:346–354. https://doi.org/10.1016/j.asoc.2019.02.036

    Article  Google Scholar 

  4. Arunkumar N, Mohammed MA, Mostafa SA, Ibrahim DA, Rodrigues JJPC, de Albuquerque VHC (2020) Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks. Concurr Comput Pract Exp 32(1):e4962. https://doi.org/10.1002/cpe.4962

    Article  Google Scholar 

  5. Şişik F, Eser S (2020) Brain tumor segmentation approach based on the extreme learning machine and significantly fast and robust fuzzy C-means clustering algorithms running on Raspberry Pi hardware. Med Hypotheses 136:109507. https://doi.org/10.1016/j.mehy.2019.109507

    Article  Google Scholar 

  6. Al-Qaisi L, Hassonah MA, Al-Zoubi MM, Al-Zoubi AM (2021) A review of evolutionary data clustering algorithms for image segmentation. Evolutionary Data Clustering: Algorithms and Applications 201–214. https://doi.org/10.1007/978-981-33-4191-3_9

  7. Shukla M, Sharma KK (2020) A comparative study to detect tumor in brain mri images using clustering algorithms. 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), IEEE. https://doi.org/10.1109/ICIMIA48430.2020.9074922

  8. Zhang X, Sun Y, Liu H, Hou Z, Zhao F, Zhang C (2021) Improved clustering algorithms for image segmentation based on non-local information and back projection. Inf Sci 550:129–144. https://doi.org/10.1016/j.ins.2020.10.039

    Article  MathSciNet  Google Scholar 

  9. Khairandish MO, Sharma M, Jain V, Chatterjee JM, Jhanjhi NZ (2021) A Hybrid CNN-SVM Threshold Segmentation Approach for Tumor Detection and Classification of MRI Brain Images. IRBM. https://doi.org/10.1016/j.irbm.2021.06.003

    Article  Google Scholar 

  10. Maqsood S, Damasevicius R, Shah FM (2021) An Efficient Approach for the Detection of Brain Tumor Using Fuzzy Logic and U-NET CNN Classification. International Conference on Computational Science and Its Applications, Springer, Chamhttps://doi.org/10.1007/978-3-030-86976-2_8

  11. Mishra S, Mohanty S (2022) Integration of Machine Learning and IoT for Assisting Medical Experts in Brain Tumor Diagnosis. Smart Healthcare Analytics: State of the Art, Springer, Singapore, pp 133–164. https://doi.org/10.1007/978-981-16-5304-9_10

  12. Rao CS, Karunakara K (2021) A comprehensive review on brain tumor segmentation and classification of MRI images. Multimed Tools Appl 80:17611–17643. https://doi.org/10.1007/s11042-020-10443-1

    Article  Google Scholar 

  13. Sharma P, Shukla AP (2021) A Review on Brain Tumor Segmentation and Classification for MRI Images. 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), IEEE. https://doi.org/10.1109/ICACITE51222.2021.9404662

  14. Pitchai R, Supraja P, Victoria AH, Madhavi M (2021) Brain Tumor Segmentation Using Deep Learning and Fuzzy K-Means Clustering for Magnetic Resonance Images. Neural Process Lett 53(4):2519–2532. https://doi.org/10.1007/s11063-020-10326-4

    Article  Google Scholar 

  15. Chahal PK, Pandey S (2021) A hybrid weighted fuzzy approach for brain tumor segmentation using MR images. Neural Comput Appl 1–15. https://doi.org/10.1007/s00521-021-06010-w

  16. Budati AK, Katta RB (2021) An automated brain tumor detection and classification from MRI images using machine learning techniques with IoT. Environ Dev Sustain 1–15. https://doi.org/10.1007/s10668-021-01861-8

  17. Sharif MI, Khan MA, Alhussein M, Aurangzeb K, Raza M (2021) A decision support system for multimodal brain tumor classification using deep learning. Complex Intell Syst 1–14. https://doi.org/10.1007/s40747-021-00321-0

  18. Kapila D, Bhagat N (2021) Efficient feature selection technique for brain tumor classification utilizing hybrid fruit fly based abc and ann algorithm. Mater Today: Proc 12–20. https://doi.org/10.1016/j.matpr.2021.04.089

  19. Agravat RR, Raval MS (2021) A Survey and Analysis on Automated Glioma Brain Tumor Segmentation and Overall Patient Survival Prediction. Arch Comput Methods Eng 28:4117–4152. https://doi.org/10.1007/s11831-021-09559-w

    Article  Google Scholar 

  20. Krishnakumar S, Manivannan K (2021) Effective segmentation and classification of brain tumor using rough K means algorithm and multi kernel SVM in MR images. J Ambient Intell Humaniz Comput 12(6):6751–6760. https://doi.org/10.1007/s12652-020-02300-8

    Article  Google Scholar 

  21. Sasank VVS, Venkateswarlu S (2021) Brain tumor classification using modified kernel based softplus extreme learning machine. Multimed Tools Appl 80(9):13513–13534. https://doi.org/10.1007/s11042-020-10423-5

    Article  Google Scholar 

  22. Kumar DM, Satyanarayana D, Prasad MN (2021) MRI brain tumor detection using optimal possibilistic fuzzy C-means clustering algorithm and adaptive k-nearest neighbor classifier. J Ambient Intell Humaniz Comput 12(2):2867–2880. https://doi.org/10.1007/s12652-020-02444-7

    Article  Google Scholar 

  23. Kumar S, Mankame DP (2020) Optimization driven Deep Convolution Neural Network for brain tumor classification. Biocybern Biomed Eng 40(3):1190–1204. https://doi.org/10.1016/j.bbe.2020.05.009

    Article  Google Scholar 

  24. Raja PMS (2020) Brain tumor classification using a hybrid deep autoencoder with Bayesian fuzzy clustering-based segmentation approach. Biocybern Biomed Eng 40(1):440–453. https://doi.org/10.1016/j.bbe.2020.01.006

    Article  Google Scholar 

  25. Mohammadi-Balani A, Nayeri MD, Azar A, Taghizadeh-Yazdi M (2021) Golden eagle optimizer: A nature-inspired metaheuristic algorithm. Comput Ind Eng 152:107050. https://doi.org/10.1016/j.cie.2020.107050

    Article  Google Scholar 

  26. Washburn PS (2020) Investigation of severity level of diabetic retinopathy using adaboost classifier algorithm. Mater Today: Proc 33:3037–3042. https://doi.org/10.1016/j.matpr.2020.03.199

    Article  Google Scholar 

Download references

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, SR University, Warangal, Telangana, 506371, India

    Kodela Rajkumaar

  2. Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, Telangana, 500075, India

    Ravi Boda

  3. Department of Electronics and Communication Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, 500090, India

    Nagadeepa Choppakatla

  4. Department of Electronics and Communication Engineering, G.V.P. College of Engineering (A), Visakhapatnam, Andhra Pradesh, 530048, India

    S. M. K. Chaitanya

Authors
  1. Kodela Rajkumaar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ravi Boda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nagadeepa Choppakatla
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. M. K. Chaitanya
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kodela Rajkumaar.

Ethics declarations

Conflict of interest

The authors declare that they have no potential conflict of interest.

Ethical approval

All applicable institutional and/or national guidelines for the care and use of animals were followed.

Informed consent

For this type of analysis formal consent is not needed.

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

Rajkumaar, K., Boda, R., Choppakatla, N. et al. An optimized eagle adaboost model for brain tumor classification and severity analysis system. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-023-17789-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11042-023-17789-2

Keywords

Search

Navigation