Volume 22, Issue 2 (JIAEEE Vol.22 No.2 2025)
Journal of Iranian Association of Electrical and Electronics Engineers 2025, 22(2): 129-141 |
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Mavaddati S, Azhari Lamraski H S. Brain Tumor Detection and Classification Based on MRI Images Using Deep Learning and Transfer Learning Models. Journal of Iranian Association of Electrical and Electronics Engineers 2025; 22 (2) :129-141
URL: http://jiaeee.com/article-1-1782-en.html
URL: http://jiaeee.com/article-1-1782-en.html
Department of Electrical Engineering, Faculty of Engineering and Technology, University of Mazandaran
Abstract: (989 Views)
Brain tumors are among the most common and fatal types of cancer. Accurate and timely diagnosis of these tumors is essential for disease management and successful patient prognosis. Additionally, the precise identification of the tumor type is crucial in determining the treatment path. By recognizing the type of tumor, doctors can select the most appropriate treatment method, which may include surgery, radiation therapy, chemotherapy, or a combination of these methods. Furthermore, the tumor type helps predict disease progression and the quality of life post-treatment. In recent years, deep learning has been a powerful tool for various image-processing tasks, including brain tumor detection. In this paper, various deep learning models such as CNN, RNN, VGG16, InceptionV3, and ResNet101 are evaluated for classifying brain tumor types from the Figshare dataset based on MRI images of glioma, meningioma, pituitary tumors, and no tumor. Finally, a suitable deep model based on ResNet101 combined with transfer learning is proposed. Based on various metrics and statistical tests, the findings indicate that deep learning models can be effectively used for brain tumor detection. Among these, the ResNet101 model achieved an accuracy of 98.37% in classifying the four tumor classes. This study demonstrates that deep learning holds significant potential for improving brain tumor detection accuracy.
Keywords: Brain tumor detection, Deep learning, Transfer learning, Figshare dataset, Image processing, ResNet101, Deep neural network.
Type of Article: Research |
Subject:
Electronic
Received: 2024/12/25 | Accepted: 2025/03/15 | Published: 2025/08/15
Received: 2024/12/25 | Accepted: 2025/03/15 | Published: 2025/08/15
References
1. [1] R. Kaifi, "A Review of Recent Advances in Brain Tumor Diagnosis Based on AI-Based Classification", Diagnostics (Basel), Vol. 13, No. 18, Sep. 2023. [DOI:10.3390/diagnostics13183007]
2. [2] M. Toğaçar, B. Ergen, and Z. Cömert, "BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model", Medical Hypotheses, Vol. 134, 2020. [DOI:10.1016/j.mehy.2019.109531]
3. [3] K. V. Chaithanyadas, and G. R. G. King, "Brain tumor classification: a comprehensive systematic review on various constraints", Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, Vol. 11, No. 3, pp. 517-529, 2023. [DOI:10.1080/21681163.2022.2083019]
4. [4] U. Aeman, M. Kaleem, M. Sarwar, M. Azhar, et al., "A systematic literature review on classification of brain tumor detection", Journal of Computing & Biomedical Informatics, Vol. 5, No. 2, pp. 327-337, 2023.
5. [5] R. Akbari Doutepeh Sofla and M. Akhoundzadeh Hanzaei, "A deep learning-based method for brain tumor detection from magnetic resonance images," Computational Science, vol. 6, no. 2, pp. 54-64, 2021.
6. [6] M. Yousefi, "Brain tumor detection from MRI images using deep learning method," Scientific Journal of Research Studies in Basic Sciences and Future Medical Sciences, vol. 1, no. 1, 2024.
7. [7] A. Gholipour and K. Sabri Loghaei, "Brain tumor detection with deep learning and attention mechanism using multimodal MRI images," in Proceedings of the 1st International and 6th National Conference on Computer, Information Technology, and Artificial Intelligence Applications, 2022
8. [8] D. Jafarkhah Seyghalani, M. Yazdi, and M. Faghihi, "Brain tumor detection using MRI and CT image fusion based on deep learning methods," Scientific Journal of Biomedical Engineering, vol. 14, no. 4, pp. 267-276, 2020.
9. [9] Z. Khazaei, M. Langarizadeh, and M. A. Shiri Ahmadabadi, "Glioma brain tumor detection using magnetic resonance imaging with deep learning methods: A systematic review," Health and Biomedical Informatics Journal, vol. 2, no. 8, pp. 218-233, 2021.
10. [10] A. Najafzadeh and H. R. Ghaffari, "Brain tumor detection from MRI images using two-dimensional convolutional neural network," Current Internal Medicine, vol. 26, no. 4, pp. 398-413, 2020. [DOI:10.32598/hms.26.4.3303.1]
11. [11] H. El Hamdaoui, A. Benfares, S. Boujraf, et al., "High precision brain tumor classification model based on deep transfer learning and stacking concepts", Indonesian Journal of Electrical Engineering and Computer Science, Vol. 24, pp. 167-177, 2021. [DOI:10.11591/ijeecs.v24.i1.pp167-177]
12. [12] M. A. Khan, I. Ashraf, M. Alhaisoni, et al., "Multimodal brain tumor classification using deep learning and robust feature selection: A machine learning application for radiologists", Diagnostics, Vol. 10, pp. 1-19, 2020. [DOI:10.3390/diagnostics10080565]
13. [13] D. Filatov, and H. Yar, "Brain tumor diagnosis and classification via pre-trained convolutional neural networks", [Online]. Available: http://arxiv.org/abs/2208.00768, 2022. [DOI:10.1101/2022.07.18.22277779]
14. [14] A. A. Akinyelu, F. Zaccagna, J. T. Grist, M. Castelli, and L. Rundo, "Brain tumor diagnosis using machine learning, convolutional neural networks, capsule neural networks and vision transformers applied to MRI: A survey", Journal of Imaging, Vol. 8, pp. 1-40, 2022. [DOI:10.3390/jimaging8080205]
15. [15] H. Kibriya, R. Amin, A. H. Alshehri, M. Masood, S. S. Alshamrani, and A. A. Alshehri, "Novel and effective brain tumor classification model using deep feature fusion and famous machine learning classifiers", Computational Intelligence and Neuroscience, 2022. [DOI:10.1155/2022/7897669]
16. [16] Z. Al-Azzwi, and A. Nazarov, "Brain tumor classification based on improved stacked ensemble deep learning methods", Asian Pacific Journal of Cancer Prevention (APJCP), Vol. 24, pp. 2141-2148, 2023. doi: 10.31557/APJCP.2023.24.6.2141. [DOI:10.31557/APJCP.2023.24.6.2141]
17. [17] S. Deepak, and P. Ameer, "Brain tumor classification using deep CNN features via transfer learning", Computers in Biology and Medicine, Vol. 111, Art. no. 103345, 2019. doi: 10.1016/j.compbiomed.2019.103345. [DOI:10.1016/j.compbiomed.2019.103345]
18. [18] R. Jain, N. Jain, A. Aggarwal, and D. J. Hemanth, "Convolutional neural network-based Alzheimer's disease classification from magnetic resonance brain images", Cognitive Systems Research, 2019. doi: 10.1016/j.cogsys.2018.12.015. [DOI:10.1016/j.cogsys.2018.12.015]
19. [19] Z. N. K. Swati, Q. Zhao, M. Kabir, F. Ali, A. Zakir, S. Ahmad, and J. Lu, "Content-based brain tumor retrieval for MR images using transfer learning", IEEE Access, Vol. 7, pp. 17809-17822, 2019. [DOI:10.1109/ACCESS.2019.2892455]
20. [20] A. Veeramuthu, S. Meenakshi, G. Mathivanan, K. Kotecha, J. Saini, V. Vijayakumar, and V. Subramaniyaswamy, "MRI brain tumor image classification using a combined feature and image-based classifier", Frontiers in Psychology, Vol. 13, Art. no. 848784, 2022. [DOI:10.3389/fpsyg.2022.848784]
21. [21] P. Gao, et al., "Development and validation of a deep learning model for brain tumor diagnosis and classification using magnetic resonance imaging", JAMA Network Open, Vol. 5, Art. no. e2225608, 2022. [DOI:10.1001/jamanetworkopen.2022.25608]
22. [22] A. M. Alqudah, H. Alquraan, I. A. Qasmieh, A. Alqudah, and W. Al-Sharu, "Brain tumor classification using deep learning technique-A comparison between cropped, uncropped, and segmented lesion images with different sizes", International Journal of Advanced Trends in Computer Science and Engineering, Vol. 8, No. 6, pp. 3684-3691, 2020. [DOI:10.30534/ijatcse/2019/155862019]
23. [23] R. Sankaranarayanan, M. S. Kumar, B. Chidhambararajan, and P. Sirenjeevi, "Brain tumor detection and classification using VGG 16", International Conference on Artificial Intelligence, Knowledge Discovery, and Concurrent Engineering (ICECONF), Chennai, India, pp. 1-5, 2023. doi: 10.1109/ICECONF57129.2023.10083866. [DOI:10.1109/ICECONF57129.2023.10083866]
24. [24] T. S. Kumar, R. Lohanandd, K. Kaviyanandh, and J. Gunabharathi, "Brain tumor classification with Inception V3 network model using transfer learning", 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, pp. 1392-1395, 2023. doi: 10.1109/ICACCS57279.2023.10112951. [DOI:10.1109/ICACCS57279.2023.10112951]
25. [25] "Brain tumor dataset", figshare, 2020. [Online]. Available: https://figshare.com/articles/brain_tumor_dataset/1512427.
26. [26] Y. Zhou, et al., "Forecasting emerging technologies using data augmentation and deep learning", Scientometrics, Vol. 123, pp. 1-29, 2020. [DOI:10.1007/s11192-020-03351-6]
27. [27] Z. Liu, et al., "Automatic diagnosis of fungal keratitis using data augmentation and image fusion with deep convolutional neural network", Computers in Methods and Programs in Biomedicine, Vol. 187, 2020. [DOI:10.1016/j.cmpb.2019.105019]
28. [28] Z. Mushtaq, S. F. Su, and Q. V. Tran, "Spectral images based environmental sound classification using CNN with meaningful data augmentation", Applied Acoustics, Vol. 172, 2021. [DOI:10.1016/j.apacoust.2020.107581]
29. [29] M. Sajjad, et al., "Multi-grade brain tumor classification using deep CNN with extensive data augmentation", Journal of Computer Science, Vol. 30, pp. 174-182, 2019. [DOI:10.1016/j.jocs.2018.12.003]
30. [30] Q. Xiao, et al., "Deep learning-based ECG arrhythmia classification: A systematic review", Applied Sciences, Vol. 13, No. 8, 2023. [DOI:10.3390/app13084964]
31. [31] X. Li, et al., "Automatic heartbeat classification using S-shaped reconstruction and a squeeze-and-excitation residual network", Computers in Biology and Medicine, Vol. 140, 2021. [DOI:10.1016/j.compbiomed.2021.105108]
32. [32] Z. Zhong, et al., "Random erasing data augmentation", in Proceedings of the 34th AAAI Conference on Artificial Intelligence, pp. 13001-13008, 2020. [DOI:10.1609/aaai.v34i07.7000]
33. [33] D. Zhang, et al., "Interpretable deep learning for automatic diagnosis of 12-lead electrocardiogram", iScience, Vol. 24, 2021. [DOI:10.1016/j.isci.2021.102373]
34. [34] R. Aniruddh, et al., "Data augmentation for electrocardiograms", in Conference on Health, Inference, and Learning, pp. 282-310, 2022.
35. [35] M. Gao, et al., "A transfer residual neural network based on ResNet-34 for detection of wood knot defects", Forests, Vol. 12, No. 2, Art. no. 212, 2021. doi: 10.3390/f12020212. [DOI:10.3390/f12020212]
36. [36] K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition", in Proceedings of CVPR, 2016. [DOI:10.1109/CVPR.2016.90]
37. [37]] A. Deshpande, V. Estrela, and P. Patavardhan, "The DCT-CNN-ResNet50 architecture to classify brain tumors with super-resolution, convolutional neural network, and the ResNet50", Neuroscience Informatics, Vol. 1, No. 4, 2021. [DOI:10.1016/j.neuri.2021.100013]
38. [38] Y. Gao, and K. M. Mosalam, "Deep transfer learning for image-based structural damage recognition", Computer-Aided Civil and Infrastructure Engineering, Vol. 33, pp. 748-768, 2018. [DOI:10.1111/mice.12363]
39. [39] S. Kentsch, et al., "Computer vision and deep learning techniques for the analysis of drone-acquired forest images: A transfer learning study", Remote Sensing, Vol. 12, 2020. [DOI:10.3390/rs12081287]
40. [40] P. Yan, et al., "A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions", IEEE Access, Vol. 12, pp. 3768-3789, 2024. doi: 10.1109/ACCESS.2023.3349132. [DOI:10.1109/ACCESS.2023.3349132]
41. [41] S. Mavaddati, "Classification of Brain Tumor Using Model Learning Based on Statistical and Texture Features", Journal of Iranian Association of Electrical and Electronics Engineers, Vol. 19, pp. 177-188, 2022. [DOI:10.52547/jiaeee.19.2.177]
42. [42] M. Wu, Y. Lu, W. Yang, and S. Y. Wong, "A study on arrhythmia via ECG signal classification using the convolutional neural network", Frontiers in Computational Neuroscience, Vol. 14, 2021. [DOI:10.3389/fncom.2020.564015]
43. [43] M. F. Moller, "A scaled conjugate gradient algorithm for fast supervised learning", Neural Networks, Vol. 6, pp. 525-533, 1993. [DOI:10.1016/S0893-6080(05)80056-5]
44. [44] J. Demsar, "Statistical comparisons of classifiers over multiple data sets", The Journal of Machine Learning Research, Vol. 7, pp. 1-30, 2006.
45. [45] D. J. Sheskin, Handbook of Parametric and Nonparametric Statistical Procedures, 4th ed., Boca Raton, FL: Chapman & Hall/CRC, 2000.
46. [46] N. Aghaei, G. Akbarizadeh, and A. Kosarian, "Using ShuffleNet to design a deep semantic segmentation model for oil spill detection in synthetic aperture radar images", Journal of Iranian Association of Electrical and Electronics Engineers, Vol. 19, pp. 131-144, 2022. [DOI:10.52547/jiaeee.19.3.131]
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