Superior Classification of Brain Cancer Types Through Machine Learning Techniques Applied to Magnetic Resonance Imaging

Authors

DOI:

https://doi.org/10.56294/dm2025472

Keywords:

Brain cancer, MRI, Data mining, Machine learning, Classification, Glioma, Meningioma, Neural networks, K-Nearest Neighbors, Diagnostic imaging

Abstract

Brain cancer remains one of the most challenging medical conditions due to its intricate nature and the critical functions of the brain. Effective diagnostic and treatment strategies are essential, particularly given the high stakes involved in early detection. Magnetic Resonance (MR) imaging has emerged as a crucial modality for the identification and monitoring of brain tumors, offering detailed insights into tumor morphology and behavior. Recent advancements in artificial intelligence (AI) and machine learning (ML) have revolutionized the analysis of medical imaging, significantly enhancing diagnostic precision and efficiency. This study classifies three primary brain tumor types—glioma, meningioma, and general brain tumors—utilizing a comprehensive dataset comprising 15,000 MR images obtained from Kaggle. We evaluated the performance of six distinct machine learning models: K-Nearest Neighbors (KNN), Neural Networks, Logistic Regression, Support Vector Machine (SVM), Decision Trees, and Random Forests. Each model's effectiveness was assessed through multiple metrics, including classification accuracy (CA), Area Under the Curve (AUC), F1 score, precision, and recall. Our findings reveal that KNN and Neural Networks achieved remarkable classification accuracies of 98.5% and 98.4%, respectively, significantly surpassing the performance of other evaluated models. These results underscore the promise of ML algorithms, particularly KNN and Neural Networks, in improving the diagnostic process for brain cancer through MR imaging. Future research will focus on validating these models with real-world clinical data, aiming to refine and enhance diagnostic methodologies, thus contributing to the development of more accurate, efficient, and accessible tools for brain cancer diagnosis and management.

References

1. Alzboon MS, Al-Batah M, Alqaraleh M, Abuashour A, Bader AF. A Comparative Study of Machine Learning Techniques for Early Prediction of Diabetes. In: 2023 IEEE 10th International Conference on Communications and Networking, ComNet 2023 - Proceedings. 2023. p. 1–12.

2. Alzboon MS, Al-Batah M, Alqaraleh M, Abuashour A, Bader AF. A Comparative Study of Machine Learning Techniques for Early Prediction of Prostate Cancer. In: 2023 IEEE 10th International Conference on Communications and Networking, ComNet 2023 - Proceedings. 2023. p. 1–12.

3. Al-shanableh N, Alzyoud M, Al-husban RY, Alshanableh NM, Al-Oun A, Al-Batah MS, et al. Advanced Ensemble Machine Learning Techniques for Optimizing Diabetes Mellitus Prognostication: A Detailed Examination of Hospital Data. Data Metadata. 2024;3:363.

4. Al-Batah MS, Salem Alzboon M, Solayman Migdadi H, Alkhasawneh M, Alqaraleh M. Advanced Landslide Detection Using Machine Learning and Remote Sensing Data. Data Metadata [Internet]. 2024 Oct 7;3. Available from: https://dm.ageditor.ar/index.php/dm/article/view/419/782

5. Wahed MA, Alqaraleh M, Alzboon MS, Al-Batah MS. Application of Artificial Intelligence for Diagnosing Tumors in the Female Reproductive System: A Systematic Review. Multidiscip. 2025;3:54.

6. Alqaraleh M, Abdel M. Advancing Medical Image Analysis : The Role of Adaptive Optimization Techniques in Enhancing COVID-19 Detection , Lung Infection , and Tumor Segmentation Avances en el análisis de imágenes médicas : el papel de las técnicas de optimización adaptativa para. LatIA. 2024;2(74).

7. Alzboon MS, Alqaraleh M, Al-Batah MS. AI in the Sky: Developing Real-Time UAV Recognition Systems to Enhance Military Security. Data Metadata. 2024;3(417).

8. Sajjad M, Khan S, Muhammad K, Wu W, Ullah A, Baik SW. Multi-grade brain tumor classification using deep CNN with extensive data augmentation. J Comput Sci. 2019;30:174–82.

9. Arimura H, Suzuki K, Ishikawa K. Machine learning in medical imaging: brain tumor classification using convolutional neural networks. Comput Methods Programs Biomed. 2020;197:105735.

10. Das SK, Roy P, Mishra AK. Recognition of ischaemia and infection in diabetic foot ulcer: A deep convolutional neural network based approach. Int J Imaging Syst Technol. 2022;32(1):192–208.

11. Babu RS, Sunil KD, Arora P. Performance evaluation of KNN algorithm in brain tumor detection using MRI images. J Adv Res Med Imaging. 2022;15(1):47–55.

12. Shankar A, Yadav N, Rathi V. Comparative study of neural network models in brain cancer detection using MRI images. J Med Syst. 2020;44(5):1–10.

13. Muhyeeddin Alqaraleh, Mohammad Al-Batah, Mowafaq Salem Alzboon EA. Automated quantification of vesicoureteral reflux using machine learning with advancing diagnostic precision. Data Metadata. 2025;4:460.

14. Mohammad Al-Batah, Mowafaq Salem Alzboon, Muhyeeddin Alqaraleh FA. Comparative Analysis of Advanced Data Mining Methods for Enhancing Medical Diagnosis and Prognosis. Data Metadata. 2024;3:465.

15. Ahmad A, Alzboon MS, Alqaraleh MK. Comparative Study of Classification Mechanisms of Machine Learning on Multiple Data Mining Tool Kits. Am J Biomed Sci Res 2024 [Internet]. 2024;22(1):577–9. Available from: www.biomedgrid.com

16. Alzboon MS, Al-Batah MS, Alqaraleh M, Abuashour A, Bader AFH. Early Diagnosis of Diabetes: A Comparison of Machine Learning Methods. Int J online Biomed Eng. 2023;19(15):144–65.

17. Al-Batah MS, Alzboon MS, Alzyoud M, Al-Shanableh N. Enhancing Image Cryptography Performance with Block Left Rotation Operations. Appl Comput Intell Soft Comput. 2024;2024(1):3641927.

18. Wahed MA, Alqaraleh M, Alzboon MS, Al-Batah MS. Evaluating AI and Machine Learning Models in Breast Cancer Detection: A Review of Convolutional Neural Networks (CNN) and Global Research Trends. LatIA. 2025;3:117.

19. Al-Batah M, Zaqaibeh B, Alomari SA, Alzboon MS. Gene Microarray Cancer classification using correlation based feature selection algorithm and rules classifiers. Int J online Biomed Eng. 2019;15(8):62–73.

20. Alqaraleh M, Alzboon MS, Al-Batah MS, Wahed MA, Abuashour A, Alsmadi FH. Harnessing Machine Learning for Quantifying Vesicoureteral Reflux: A Promising Approach for Objective Assessment. Int J Online & Biomed Eng. 2024;20(11).

21. Al-Batah MS, Alzboon MS, Alazaidah R. Intelligent Heart Disease Prediction System with Applications in Jordanian Hospitals. Int J Adv Comput Sci Appl. 2023;14(9):508–17.

22. Alzboon MS. Internet of things between reality or a wishing-list: a survey. Int J Eng & Technol. 2018;7(2):956–61.

23. Alzboon MS, Qawasmeh S, Alqaraleh M, Abuashour A, Bader AF, Al-Batah M. Machine Learning Classification Algorithms for Accurate Breast Cancer Diagnosis. In: 2023 3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023. 2023.

24. Alzboon MS, Aljarrah E, Alqaraleh M, Alomari SA. Nodexl Tool for Social Network Analysis. Vol. 12, Turkish Journal of Computer and Mathematics Education. 2021.

25. Alzboon MS, Al-Batah MS. Prostate Cancer Detection and Analysis using Advanced Machine Learning. Int J Adv Comput Sci Appl. 2023;14(8):388–96.

26. Alzboon MS, Qawasmeh S, Alqaraleh M, Abuashour A, Bader AF, Al-Batah M. Pushing the Envelope: Investigating the Potential and Limitations of ChatGPT and Artificial Intelligence in Advancing Computer Science Research. In: 2023 3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023. 2023.

27. Alzboon M. Semantic Text Analysis on Social Networks and Data Processing: Review and Future Directions. Inf Sci Lett. 2022;11(5):1371–84.

28. Alzboon MS. Survey on Patient Health Monitoring System Based on Internet of Things. Inf Sci Lett. 2022;11(4):1183–90.

29. Alzboon M, Alomari SA, Al-Batah MS, Banikhalaf M. The characteristics of the green internet of things and big data in building safer, smarter, and sustainable cities. Int J Eng & Technol. 2017;6(3):83–92.

30. Al Tal S, Al Salaimeh S, Ali Alomari S, Alqaraleh M. The modern hosting computing systems for small and medium businesses. Acad Entrep J. 2019;25(4):1–7.

31. Alzboon MS, Bader AF, Abuashour A, Alqaraleh MK, Zaqaibeh B, Al-Batah M. The Two Sides of AI in Cybersecurity: Opportunities and Challenges. In: Proceedings of 2023 2nd International Conference on Intelligent Computing and Next Generation Networks, ICNGN 2023. 2023.

32. Alomari SA, Alqaraleh M, Aljarrah E, Alzboon MS. Toward achieving self-resource discovery in distributed systems based on distributed quadtree. J Theor Appl Inf Technol. 2020;98(20):3088–99.

33. Alazaidah R, Al-Qerem A, Qasem MH, Al-Shaikh A, Almilli N, Injadat MN. Feature Selection in Associative Classification-A Review and Comparative Analysis. In: 2023 24th International Arab Conference on Information Technology, ACIT 2023. 2023. p. 1–5.

34. Kapoor S, Dhull V, Sharma A, Goyal C, Verma A. A Comparative Study on Deep Learning and Machine Learning Models for Human Action Recognition in Aerial Videos. Int Arab J Inf Technol. 2023;20(4):567–74.

35. Alazaidah R. A Comparative Analysis of Discretization Techniques in Machine Learning. In: 2023 24th International Arab Conference on Information Technology, ACIT 2023. 2023. p. 1–6.

36. Kapoor S, Sharma A, Verma A, Dhull V, Goyal C. A comparative study on deep learning and machine learning models for human action recognition in aerial videos. Int Arab J Inf Technol. 2023;20(4):567–74.

37. Sharma S, Challa RK, Kumar R. An ensemble-based supervised machine learning framework for android ransomware detection. Int Arab J Inf Technol. 2021;18(3 Special Issue):422–9.

38. Alazaidah R, Owida HA, Alshdaifat N, Issa A, Abuowaida S, Yousef N. A comprehensive analysis of eye diseases and medical data classification. TELKOMNIKA (Telecommunication Comput Electron Control. 2024;22(6):1422–30.

39. Allam M, Malaiyappan N. Hybrid Feature Selection based on BTLBO and RNCA to Diagnose the Breast Cancer. Int Arab J Inf Technol. 2023;20(5):727–37.

40. Alzyoud M, Alazaidah R, Aljaidi M, Samara G, Qasem MH, Khalid M, et al. Diagnosing diabetes mellitus using machine learning techniques. Int J Data Netw Sci. 2024;8(1):179–88.

41. Alawneh H, Hasasneh A. Survival Prediction of Children after Bone Marrow Transplant Using Machine Learning Algorithms. Int Arab J Inf Technol. 2024;21(3):394–407.

Downloads

Published

2025-01-01

Issue

Section

Original

How to Cite

1.
Al-Batah M, Salem Alzboon M, Alqaraleh M. Superior Classification of Brain Cancer Types Through Machine Learning Techniques Applied to Magnetic Resonance Imaging. Data and Metadata [Internet]. 2025 Jan. 1 [cited 2024 Dec. 26];4:472. Available from: https://dm.ageditor.ar/index.php/dm/article/view/472