Evaluation of Machine Learning Algorithms for Predicting Tumor and Non-tumor Brain Mri Images

Document Type : Original Article(s)

Authors

1 Assistant Professor, Department of Radiology Technology, School of Medical Sciences, Shoushtar Faculty of Medical Sciences, Shoushtar, Iran

2 Assistant Professor, Department of Basic Sciences, School of Medical Sciences, Shoushtar Faculty of Medical Sciences, Shoushtar, Iran

3 Assistant Professor, Department of Nursing Education, School of Medical Sciences, Shoushtar Faculty of Medical Sciences, Shoushtar, Iran

4 Associate Professor, Department of Medical Physics, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

5 Shoushtar Faculty of Medical Sciences, Shoushtarو Iran

Abstract

Background: Early diagnosis of brain tumors using MRI and artificial intelligence algorithms is fundamental in improving treatment results. MRI images serve as the primary tool for identifying brain tumors. This study aims to evaluate machine learning algorithms for diagnosing brain tumors and non-tumors using MRI images.
Methods: From kaggle.com a total of 2400 MRI images were collected, and a pre-processing step was performed on them. Algorithms such as logistic regression, decision tree, random forest, simple Bayes method, support vector machine, and K nearest neighbor were also implemented on the images.
Findings: After applying all the algorithms, the values of training accuracy, test accuracy, accuracy, readability, F1 score, confusion matrix, and the area under the rocking curve were obtained to evaluate the performance criteria.
Conclusion: The investigations indicated that logistic regression and random forest algorithms performed the best. Naive Bayes and decision tree algorithms need improvement.

Highlights

Maryam Erfaninejad: Google Scholar

Sima Hashemi: Google Scholar

Nahid Chegeni: Google Scholar

Barat Barati: Google Scholar

Keywords

Main Subjects

References

  1. Krishnapriya S, Karuna Y. Pre-trained deep learning models for brain MRI image classification. Front Hum Neurosci 2023; 17: 1150120.
  2. Asad R, Rehman Su, Imran A, Li J, Almuhaimeed A, Alzahrani A. Computer-aided early melanoma brain-tumor detection using deep-learning approach. Biomedicines 2023; 11(1): 184.
  3. Reza AW, Hossain MS, Wardiful MA, Farzana M, Ahmad S, Alam F, et al. A CNN-Based strategy to classify MRI-based brain tumors using deep convolutional network. Appl Sci 2023; 13(1): 312.
  4. Thamarai M, Dhivyaa S. Analysis of Brain Tumor Classification using Pre-Trained CNN models. Heliyon2024; 10(17): e36773.
  5. Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annual review of biomedical engineering. Annu Rev Biomed Eng 2017; 19: 221-48.
  6. Kuraparthi S, Reddy MK, Sujatha C, Valiveti H, Duggineni C, Kollati M, et al. Brain tumor classification of MRI images using deep convolutional neural network. Traitement du signal 2021; 38(4): 1171-9.
  7. Nayeem MAH, Shakil MH, Afrin S, Shanto SA, Mumu SJ, Shanto MMHJIJoR, et al. A Deep Learning Based Classification Model for the Detection of Brain Tumor using MRI. Journal of Research and Innovation in Applied Science 2022; 7(9): 37-42.
  8. Sharma K, Kaur A, Gujral S. Brain Tumor Detection based on Machine Learning Algorithms. International Journal of Computer Applications 2014; 103(1): 7-11.
  9. Remzan N, Karim T, Farchi A. Brain tumor classification in magnetic resonance imaging images using convolutional neural network. International Journal of Electrical and Computer Engineering. 2022; 12(6): 6664-74.
  10. Murali E, Meena K. A novel approach for classification of brain tumor using R- International Journal of Engineering Applied Sciences and Technology 2019; 4(4): 360-4.
  11. Ghosh A, Kole A. A comparative study of enhanced machine learning algorithms for brain tumor detection and classification. [online] 2021. Available from: https://www.techrxiv.org/doi/full/10.36227/techrxiv.16863136.v1
  12. Akinyelu AA, Zaccagna F, Grist JT, Castelli M, Rundo LJJoi. Brain tumor diagnosis using machine learning, convolutional neural networks, capsule neural networks and vision transformers, applied to MRI: a survey. J Imaging 2022; 8(8): 205.
  13. Praveena S, Singh SP, Kumar BS. Machine learning mechanism for segmentation, progressive assessment and prediction of brain tumor growth. International journal of health sciences. Health Sciences 2022; 6(S2): 7696-709.
  14. Srinivas B, Rao SG. A hybrid CNN-KNN model for MRI brain tumor classification. International Journal of Recent Technology and Engineering 2019; 8(2): 5230-5.
  15. Kaur P, Raja M, Lone A, Kaur M, Sansoya N. Exploring machine learning approaches for predicting brain tumors: a comparative study. International Journal of Membrane Science and Technology 2023; 10(5): 364-74.
  16. Wang Z, Xiao X, He K, Wu D, Pang P, Wu T. A study of MRI-based machine-learning methods for glioma grading. Int J Radiat Res 2022; 20(1): 115-20.
  17. Seethalakshmi BJB. Brain Tumor Malignancy Prediction Using Machine Learning Techniques. Irish Interdisciplinary Journal of Science & Research 2024; 8(2): 86-93.
  18. Güler M, Namlı E. Brain Tumor Detection with Deep Learning Methods’ Classifier Optimization Using Medical Images. Appl Sci 2024; 14(2): 642.
  19. Wasule V, Sonar P, editors. Classification of brain MRI using SVM and KNN classifier. Proceedings of the 3rd International Conference on Sensing, Signal Processing and Security (ICSSS); 2017 4-5 May; 2017.
  20. Yu Z, He Q, Yang J, Luo M. A Supervised ML Applied Classification Model for Brain Tumors MRI. Front Pharmacol 2022; 13: 884495.
  21. Lamrani D, Cherradi B, el Gannour O, Bouqentar M, Bahatti L. Brain tumor detection using MRI Images and convolutional neural network. International Journal of Advanced Computer Science and Applications 2022; 13(7).
  22. Imam R, Alam MT. Optimizing Brain Tumor Classification: A Comprehensive Study on Transfer Learning and Imbalance Handling in Deep Learning Models. In: Cuzzolin F, Sultana M (Editors). Epistemic Uncertainty in Artificial Intelligence. Pittsburgh, PA: 2023.
  23. Chen H, Wang N, Du X, Mei K, Zhou Y, Cai G. Classification Prediction of Breast Cancer Based on Machine Learning. Comput Intell Neurosci 2023; 2023: 6530719.
  24. Win KY, Maneerat N, Choomchuay S, Sreng SKH. Suitable Supervised Machine Learning Techniques For Malignant Mesothelioma Diagnosis. Proceedings of the 11th Biomedical Engineering International Conference (BMEiCON); 2018 21-24 Nov; 2018.
Journal of Isfahan Medical School
Volume 42, Issue 778
2nd Week, October
September and October 2024
Pages 674-686

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History

  • Receive Date: 12 August 2024
  • Accept Date: 30 October 2024

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