Detection and Classification of Brain Tumor from MRI and CT Images Using Harmony Search Optimization and Deep Learning

Year : 2024 | Volume : 11 | Issue : 03 | Page : 31 49
    By

    Shaik Karimullah,

  • Ali H. Wheeb,

  • Fahimuddin Shaik,

  1. Associate Professor, Department of Electronics & Communication Engineering, Annamacharya Institute of Technology & Sciences, Rajampet, Andhra Pradesh, India
  2. Associate Professor, Department of Computer Engineering, University of Baghdad, Baghdad, Iraq
  3. Associate Professor, Department of Electronics & Communication Engineering, Annamacharya Institute of Technology & Sciences, Rajampet, Andhra Pradesh, India

Abstract

Primary brain tumor detection and classification are critical factors in ensuring effective treatment and, ultimately, improving patient well-being. This paper describes a novel method for detecting and classifying brain tumors with the help of magnetic resonance imaging (MRI) and computed tomography (CT) images. The suggested method combines harmony search optimization (HSO) and Convolution Neural Networks (CNN) based on deep learning techniques, yielding an impressive accuracy rate of 99.13% for both detection and classification tasks. Furthermore, the system has exceptional specificity (99.2243%) and sensitivity (99.245%), highlighting its precision in distinguishing true negatives and true positives. This incorporation of advanced methodologies not only improves diagnostic accuracy but also reduces reliance on radiologists’ expertise. The experimental results support the proposed approach’s effectiveness, demonstrating its ability to detect and classify brain tumors from medical images. This breakthrough is a noteworthy step forward in the fields of medical image analysis and brain tumor diagnosis, paving the way for better patient care and treatment outcomes.

Keywords: MRI and CT images, convolutional neural network (CNN), harmony search optimization

[This article belongs to Journal of Artificial Intelligence Research & Advances ]

How to cite this article:
Shaik Karimullah, Ali H. Wheeb, Fahimuddin Shaik. Detection and Classification of Brain Tumor from MRI and CT Images Using Harmony Search Optimization and Deep Learning. Journal of Artificial Intelligence Research & Advances. 2024; 11(03):31-49.
How to cite this URL:
Shaik Karimullah, Ali H. Wheeb, Fahimuddin Shaik. Detection and Classification of Brain Tumor from MRI and CT Images Using Harmony Search Optimization and Deep Learning. Journal of Artificial Intelligence Research & Advances. 2024; 11(03):31-49. Available from: https://journals.stmjournals.com/joaira/article=2024/view=170500


References

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209-49. DOI: 10.3322/caac.21660.
  2. McNeill KA. Epidemiology of brain tumors. Neurol Clin. 2016;34:981-98. DOI: 10.1016/j.ncl.2016.06.014.
  3. Johnson KJ, Cullen J, Barnholtz-Sloan JS, Ostrom QT, Langer CE, Turner MC, et al. Childhood brain tumor epidemiology: A brain tumor epidemiology consortium review. Cancer Epidemiol Biomarkers Prev. 2014;23:2716-36. DOI: 10.1158/1055-9965.EPI-14-0207.
  4. Khambhata K. Multiclass classification of brain tumor in MR images. Int J Innov Res Comput Commun Eng. 2016;4:8982-92.
  5. Madhukumar S, Santhiyakumari N. Evaluation of k-Means and fuzzy C-means segmentation on MR images of brain. Egypt J Radiol Nucl Med. 2015;46:475-9. DOI: 10.1016/j.ejrnm.2015.02.008.
  6. Das V, Rajan J. Techniques for MRI brain tumor detection: A survey. Int J Res Comput Appl Inf Technol. 2016;4:53-6.
  7. Karimullah S, Vishnuvardhan D. Experimental analysis of optimization techniques for placement and routing in Asic design. In: ICDSMLA 2019. Proc 1st Int Conf Data Sci Mach Learn Appl. Singapore: Springer; 2020. p. 908-17. DOI: 10.1007/978-981-15-1420-3_99.
  8. Işın A, Direkoğlu C, Şah M. Review of MRI-based brain tumor image segmentation using deep learning methods. Procedia Comput Sci. 2016;102:317-24. DOI: 10.1016/j.procs.2016.09.407.
  9. Kong Y, Deng Y, Dai Q. Discriminative clustering and feature selection for brain MRI segmentation. IEEE Signal Process Lett. 2015;22:573-7. DOI: 10.1109/LSP.2014.2364612.
  10. Chen H, Zou Q, Wang Q. Clinical manifestations of ultrasonic virtual reality in the diagnosis and treatment of cardiovascular diseases. J Healthc Eng. 2021;2021:1746945. DOI: 10.1155/2021/
  11. Avşar E, Salçin K. Detection and classification of brain tumours from MRI images using faster R-CNN. Teh Glasnik. 2019;13:337-42. DOI: 10.31803/tg-20190712095507.
  12. Aslam A, Khan E, Beg MMS. Improved edge detection algorithm for brain tumor segmentation. Procedia Comput Sci. 2015;58:430-7. DOI: 10.1016/j.procs.2015.08.057.
  13. Bauer S. Medical image analysis and image-based modeling for brain tumor studies [PhD dissertation]. Università ̈T Bern; 2013.
  14. Shaik F, Sharma AK, Ahmed SM. Detection and analysis of diabetic myonecrosis using an improved hybrid image processing model. In: Proc 2nd Int Conf Adv Electr Electron Inf Commun Bio-Informatics (AEEICB). IEEE Publications; 2016. p. 314-7. DOI: 10.1109/AEEICB.2016.
  15. Saba T, Mohamed AS, El-Affendi M, Amin J, Sharif M. Brain tumor detection using fusion of hand crafted and deep learning features. Cogn Syst Res. 2020;59:221-30. DOI: 10.1016/j.cogsys.2019.007.
  16. Karimullah S, Vishnuvardhan D, Arif M, Gunjan VK, Shaik F, Siddiquee KN. An improved harmony search approach for block placement for VLSI design automation. Wireless Commun Mob Comput. 2022;2022:1-10. DOI: 10.1155/2022/3016709.
  17. Gordon R, Bender R, Herman GT. Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and X-ray photography. J Theor Biol. 1970;29:471-81. DOI: 10.1016/0022-5193(70)90109-8.
  18. Mishra PK, Satapathy SC, Rout M. Segmentation of MRI brain tumor image using optimization based deep convolutional neural networks (DCNN). Open Comput Sci. 2021;11:380-90. DOI: 10.1515/comp-2020-0166.
  19. Irmak E. Multi-classification of brain tumor MRI images using deep convolutional neural network with fully optimized framework. Iran J Sci Technol Trans Electr Eng. 2021;45:1015-36. DOI: 10.1007/s40998-021-00426-9.
  20. Datta S, Chakraborty M. Brain tumor detection from preprocessed MR images using segmentation techniques. In: IJCA Special Issue on 2nd National Conference Computing, Communication and Sensor Network; 2011: 1–5.
  21. Karimullah S, Vishnuvardhan D. Pin density technique for congestion estimation and reduction of optimized design during placement and routing. Appl Nanosci. 2023;13:1819-28. DOI: 10.1007/s13204-021-02173-z.
  22. Sharma AK, Tiwari S, Aggarwal G, Goenka N, Kumar A, Chakrabarti P, et al. Dermatologist-level classification of skin cancer using cascaded ensembling of convolutional neural network and handcrafted features based deep neural network. IEEE Access. 2022;10:17920-32. DOI: 10.1109/ACCESS.2022.3149824.
  23. Zhang J, Zhou H, Niu Y, Lv J, Chen J, Cheng Y. CNN and multi-feature extraction based denoising of CT images. Biomed Signal Process Control. 2021;67:102545. DOI: 10.1016/j.bspc.2021.
  24. Hossain T, Shishir FS, Ashraf M, Al Nasim MA, Shah FM. Brain tumor detection using convolutional neural network. In: 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT). IEEE Publications; 2019. p. 1-6. DOI: 10.1109/ICASERT.2019.8934561.
  25. Khalil HA, Darwish S, Ibrahim YM, Hassan OF. 3D-MRI brain tumor detection model using modified version of level set segmentation based on dragonfly algorithm. Symmetry. 2020;12:1256. DOI: 10.3390/sym12081256.
  26. Aggarwal M, Tiwari AK, Sarathi MP, Bijalwan A. An early detection and segmentation of brain tumor using deep neural network. BMC Med Inform Decis Mak. 2023;23:78. DOI: 10.1186/s12911-023-02174-8.
  27. Geetha M, Prasanna Lakshmi K, Arumugam SR, Sandhya N. Conditional random field-recurrent neural network segmentation with optimized deep learning for brain tumour classification using magnetic resonance imaging. Imaging Sci J. 2023;71(3):199-220. DOI: 10.1080/13682199.2178611.
  28. Arunachalam S, Sethumathavan G. An effective tumor detection in MR brain images based on deep CNN approach: I- YOLOV5. Appl Artif Intell. 2022;36:2151180. DOI: 10.1080/08839514.2151180.
  29. Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, et al. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Trans Med Imaging. 2015;34:1993-2024. DOI: 10.1109/TMI.2014.2377694.
  30. Saeedi S, Rezayi S, Keshavarz H, Niakan Kalhori SR. MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques. BMC Med Inform Decis Mak. 2023;23:16. DOI: 10.1186/s12911-023-02114-6.
  31. Rezayi S, Mohammadzadeh N, Bouraghi H, Saeedi S, Mohammadpour A. Timely diagnosis of acute lymphoblastic leukemia using artificial intelligence-oriented deep learning methods. Comput Intell Neurosci. 2021;2021:5478157. DOI: 10.1155/2021/5478157.
Regular Issue Subscription Review Article
Volume 11
Issue 03
Received 21/08/2024
Accepted 02/09/2024
Published 05/09/2024
423

Login


My IP

PlumX Metrics