AccScience Publishing / IJOSI / Volume 8 / Issue 4 / DOI: 10.6977/IJoSI.202412_8(4).0006
Submit to IJOSI
Cite this article
0
Views
Journal Browser
Volume | Year
Issue
Search
Forthcoming Issue
Current Issue
View All
News and Announcements
View All
ARTICLE

Brain tumor detection using MRI images- a comparative study based on different classifiers

Suvarna Raju Puligurti1* ,  P. Chitra1 ,  A.V. Bharadwaja2
Show Less
1 Department of Electronics and Communication EngineeringSathyabama Institute of Science & Technology Chennai, Tamil Nadu 600119, India
2 Vignan'sInstitute of Information TechnologyBesides VSEZ Vadlapudi Duvvada, Gajuwaka, Visakhapatnam, Andhra Pradesh 530049
IJOSI 2024 , 8(4), 85–102; https://doi.org/10.6977/IJoSI.202412_8(4).0006
Submitted: 28 February 2024 | Revised: 26 April 2024 | Accepted: 15 June 2024 | Published: 30 December 2024
© 2024 by the Author(s). Licensee AccScience Publishing, USA. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC BY-NC 4.0) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Download PDF
Cite
XML
HTML
Abstract

The detection of brain tumors is a major challenge in clinical imaging. Integrating machine learning techniques with MRI (Magnetic Resonance Imaging) analysis has been revealed as a powerful and exciting strategy. This study high-lights the importance of early detection and precise diagnosis for medical intervention. Machine learning models extract MRI features like texture, shape, and intensity, and train on labelled datasets. The paper discusses the ad-vantages and challenges of this approach, emphasizing data quality, feature engineering, and model selection. It also highlights the potential for continuous improvement in machine learning models. The synergy between machine learn-ing and MRI imaging holds promise for improved patient outcomes and diagnostic processes. This study compares the techniques of ML, DL and Hybrid Learning. This comparative analysis demonstrates that hybrid Learning per-forms better in identifying BTs on MRI images. Each system produced superior results. Especially, deep CNN+SVM+RBF combined technique yields best performance, with 98.6% accuracy, 98.2% sensitivity, and 98.9% specificity.

Keywords
Brain Tumour (BT)
MRI images
Machine Learning
Deep Learning
and Hybrid Machine-Deep Learning.
References
  1. Ahmad, S., & Choudhury, P. K. (2022). On the Performance of Deep Transfer Learning Networks for Brain Tumor Detection Using MR Images. *IEEE Access*, 10, 59099-59114.
  2. Aiwale, P., & Ansari, S. (2019). Brain Tumor Detection Using KNN. *International Journal of Scientific & Engineering Research*, 10(12), 187-193.
  3. Al-Zurfi, A. N. H. (2019). An Automated Classification System to Determine Malignant Grades of Brain Tumor (Glioma) in Magnetic Resonance Images Based on Meta-Trainable Multiple Classifier Schemes. University of Salford (United Kingdom).
  4. Amin, J., Sharif, M., Haldorai, A., Yasmin, M., & Nayak, R. S. (2021). Brain Tumor Detection and Classification Using Machine Learning: A Comprehensive Survey. *Complex & Intelligent Systems*, 1-23.
  5. Anand, L., Rane, K. P., Bewoor, L. A., Bangare, J. L., Surve, J., Raghunath, M. P., ... & Osei, B. (2022). Development of Machine Learning and Medical-Enabled Multimodal for Segmentation and Classification of Brain Tumors Using MRI Images. *Computational Intelligence and Neuroscience*, 2022.
  6. Archana, K. V., & Komarasamy, G. (2023). A Novel Deep Learning-Based Brain Tumor Detection Using the Bagging Ensemble with K-Nearest Neighbour. *Journal of Intelligent Systems*, 32(1), 20220206.
  7. Athira, V. S., Dhas, A. J., & Sreejamole, S. S. (2015). Brain Tumor Detection and Segmentation in MR Images Using GLCM and AdaBoost Classifier. *International Journal of Engineering Research*, 3, 32-36.
  8. Avazzadeh, Z., Hassani, H., Ebadi, M. J., Agarwal, P., Poursadeghfard, M., & Naraghirad, E. (2023). Optimal approximation of fractional order brain tumor model using generalized Laguerre polynomials,Iranian journal of science,47(2), 501-513.
  9. Brindha, P. G., Kavinraj, M., Manivasakam, P., & Prasanth, P. (2021, February). Brain Tumor Detection from MRI Images Using Deep Learning Techniques. In *IOP Conference Series: Materials Science and Engineering*, 1055(1), 012115. IOP Publishing.
  10. Dash, R., Majhi, B., & Zhang, Y. (2019). A Hybrid Regularized Extreme Learning Machine for Automated Detection of the Pathological Brain. *Biocybernetics and Biomedical Engineering*, 39(3), 880-892.
  11. Febrianto, D. C., Soesanti, I., & Nugroho, H. A. (2020). Convolutional Neural Network for Brain Tumor Detection. *IOP Conf. Ser.: Mater. Sci. Eng.*, 771(1), 012031. doi:10.1088/1757-899X/771/1/012031.
  12. Garg, G., & Garg, R. (2021). Brain Tumor Detection and Classification Based on Hybrid Ensemble Classifier. *arXiv preprint arXiv:2101.00216*.
  13. Gurbină, M., Lascu, M., & Lascu, D. (2019, July). Tumor Detection and Classification of MRI Brain Images Using Different Wavelet Transforms and Support Vector Machines. In *2019 42nd International Conference on Telecommunications and Signal Processing (TSP)*, 505-508. IEEE.
  14. Győrfi, Á., Kovács, L., & Szilágyi, L. (2019, October). Brain Tumor Detection and Segmentation from Magnetic Resonance Image Data Using Ensemble Learning Methods. In *2019 IEEE International Conference on Systems, Man, and Cybernetics (SMC)*, pp. 909-914. IEEE.
  15. Hannan, S. A., Hivre, M., Lata, M., Krishna, B. H., Sathyasiva, S., & Arshad, M. W. (2022). Brain Damage Detection Using Machine Learning Approach. *International Journal of Health Sciences*, Special, (7), 4910-4924.
  16. Haq, E. U., Jianjun, H., Huarong, X., Li, K., & Weng, L. (2022). A Hybrid Approach Based on Deep CNN and Machine Learning Classifiers for Tumor Segmentation and Classification in Brain MRI. *Computational and Mathematical Methods in Medicine*, 2022.
  17. Francisco, J. P., Mario, Z. M., & Miriam, R. A. (2021). A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network. *Healthcare*, 9(2), 153.
  18. Jahromi, Z. F., Javeri, A., & Taha, M. F. (2019). Tumor Suppressive Effects of the Pleiotropically Acting miR-195 in Colorectal Cancer Cells. *EXCLI Journal*, 18, 243.
  19. Jia, Z., & Chen, D. (2020). Brain Tumor Identification and Classification of MRI Images Using Deep Learning Techniques. *IEEE Access*.
  20. Jiang, S., Gu, Y., & Kumar, E. (2023). Magnetic Resonance Imaging (MRI) Brain Tumor Image Classification Based on Five Machine Learning Algorithms. *Cloud Computing and Data Science*, 122-133.
  21. Kavitha, A. R., & Chellamuthu, C. (2013). Detection of Brain Tumor from MRI Image Using Modified Region Growing and Neural Network. *The Imaging Science Journal*, 61(7), 556-567.
  22. Kaur, P., Singh, G., & Kaur, P. (2020). Classification and Validation of MRI Brain Tumor Using Optimized Machine Learning Approach. In *ICDSMLA 2019: Proceedings of the 1st International Conference on Data Science, Machine Learning and Applications*, 172-189. Springer Singapore.
  23. Krauze, A. V., Zhuge, Y., Zhao, R., Tasci, E., & Camphausen, K. (2022). AI-Driven Image Analysis in Central Nervous System: Traditional Machine Learning, Deep Learning, and Hybrid Models. *Journal of Biotechnology and Biomedicine*, 5(1), 1.
  24. Kurian, S. M., Devaraj, S. J., & Vijayan, V. P. (2021). Brain Tumour Detection by Gamma DeNoised Wavelet Segmented Entropy Classifier. *Computers, Materials & Continua*, 69(2).
  25. Lah, T. T., Novak, M., & Breznik, B. (2020, February). Brain Malignancies: Glioblastoma and Brain Metastases. In *Seminars in Cancer Biology*, 60, 262-273. Academic Press.
  26. Lamrani, D., Cherradi, B., El Gannour, O., Bouqentar, M. A., & Bahatti, L. (2022). Brain tumor detection using MRI images and convolutional neural network,International Journal of Advanced Computer Science and Applications,13(7).
  27. Magadza, T., & Viriri, S. (2021). Deep Learning for Brain Tumor Segmentation: A Survey of the State-of-the-Art. *Journal of Imaging*, 7(2), 19.
  28. Maqsood, S., Damaševičius, R., & Maskeliūnas, R. (2022). Multi-modal Brain Tumor Detection Using Deep Neural Network and Multiclass SVM. *Medicina*, 58(8), 1090.
  29. Masood, M., Nazir, T., Nawaz, M., Mehmood, A., Rashid, J., Kwon, H. Y., & Hussain, A. (2021). A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images. *Diagnostics*, 11(5), 744.
  30. McFaline-Figueroa, J. R., & Lee, E. Q. (2018). Brain Tumors. *The American Journal of Medicine*, 131(8), 874-882.
  31. Noreen, N., Palaniappan, S., Qayyum, A., Ahmad, I., & Alassafi, M. O. (2021). Brain Tumor Classification Based on Fine-Tuned Models and the Ensemble Method. *Computers, Materials & Continua*, 67(3).
  32. Panesar, S. S., D'Souza, R. N., Yeh, F. C., & Fernandez-Miranda, J. C. (2019). Machine Learning versus Logistic Regression Methods for 2-Year Mortality Prognostication in a Small, Heterogeneous Glioma Database. *World Neurosurgery: X*, 2, 100012.
  33. Pooja, A., Shalini, D., Shiri, J. S., & Sowmiya, M. (2023). Enhancement of Brain Tumor Detection on MRI Imaging Using AlexNet. *International Journal of Research in Engineering, Science and Management*, 6(5), 132-134.
  34. Pugalenthi, R., Rajakumar, M. P., Ramya, J., & Rajinikanth, V. (2019). Evaluation and Classification of the Brain Tumor MRI Using Machine Learning Technique. *Journal of Control Engineering and Applied Informatics*.
  35. Ranjbarzadeh, R., Bagherian Kasgari, A., Jafarzadeh Ghoushchi, S., Anari, S., Naseri, M., & Bendechache, M. (2021). Brain Tumor Segmentation Based on Deep Learning and an Attention Mechanism Using MRI Multi-Modalities Brain Images. *Scientific Reports*, 11(1), 10930.
  36. Saeedi, S., Rezayi, S., Keshavarz, H., & Niakan Kalhori, R. (2023). MRI-Based Brain Tumor Detection Using Convolutional Deep Learning Methods and Chosen Machine Learning Techniques. *BMC Medical Informatics and Decision Making*, 23(1), 16.
  37. Senan, E. M., Jadhav, M. E., Rassem, T. H., Aljaloud, A. S., Mohammed, B. A., & Al-Mekhlafi, Z. G. (2022). Early Diagnosis of Brain Tumor MRI Images Using Hybrid Techniques Between Deep and Machine Learning. *Computational and Mathematical Methods in Medicine*, 2022.
  38. Shaari, H., Kevrić, J., Jukić, S., Bešić, L., Jokić, D., Ahmed, N., & Rajs, V. (2021). Deep Learning-Based Studies on Pediatric Brain Tumors Imaging: A Narrative Review of Techniques and Challenges. *Brain Sciences*, 11(6), 716.
  39. Tazinetc, T. (2021). A Robust and Novel Approach for Brain Tumor Classification Using Convolutional Neural Network. *Computational Intelligence and Neuroscience*, 2021, 2392395. doi: 10.1155/2021/2392395.
  40. Takei, H., Shinoda, J., Ikuta, S., Maruyama, T., Muragaki, Y., Kawasaki, T., & Iwama, T. (2019). The Usefulness of Positron Emission Tomography for Differentiating Gliomas According to the 2016 World Health Organization Classification of Tumors of the Central Nervous System. *Journal of Neurosurgery*, 133(4), 1010-1019.
  41. Talo, M., Yildirim, O., Baloglu, U. B., Aydin, G., & Acharya, U. R. (2019). Convolutional Neural Networks for Multi-Class Brain Disease Detection Using MRI Images. *Computerized Medical Imaging and Graphics*, 78, 101673.
  42. Telrandhe, S. R., Pimpalkar, A., & Kendhe, A. (2016, February). Detection of Brain Tumors from MRI Images by Using Segmentation & SVM. In *2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave), 1-6, IEEE.Nayak, 
  43. Ullah, S., Ahmad, M., Anwar, S., & Khattak, M. I. (2023). An Intelligent Hybrid Approach for Brain  Tumor Detection,Pakistan Journal of Engineering and Technology,6(1), 42-50.
  44. Verekar, M. A. O., & Salkar, V. (2019). Detection and Classification of Brain Tumor Using Naïve Bayes and J48. *International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR)*, 9, 19-28.
  45. Wageh, M., Amin, K. M., Zytoon, A., & Ibrahim, M. (2023). Brain Tumor Detection Based on a Combination of GLCM and LBP Features with PCA and IG. *IJCI, International Journal of Computers and Information*, 10(2), 43-53.
  46. Wanis, H. A., Møller, H., Ashkan, K., & Davies, E. A. (2021). The Incidence of Major Subtypes of Primary Brain Tumors in Adults in England 1995-2017. *Neuro-Oncology*, 23(8), 1371-1382.
  47. Yildirim, M., Cengil, E., Eroglu, Y., & Cinar, A. (2023). Detection and Classification of Glioma, Meningioma, Pituitary Tumor, and Normal in Brain Magnetic Resonance Imaging Using a Deep Learning-Based Hybrid Model. *Iran Journal of Computer Science*, 1-10.
  48. Yousefi, O., Azami, P., Sabahi, M., Dabecco, R., Adada, B., & Borghei-Razavi, H. (2022). Management of Optic Pathway Glioma: A Systematic Review and Meta-Analysis. *Cancers*, 14(19), 4781.
Share
Back to top
International Journal of Systematic Innovation, Electronic ISSN: 2077-8767 Print ISSN: 2077-7973, Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept