Synthetic MRI as an Alternative Training Resource for Brain Tumor Classification: A GAN-Based Study

Öz

Accurate classification of brain tumors from magnetic resonance (MR) imaging data very important for clinical decision support systems. However, the privacy issues encountered in MR data and the high cost of data collection processes make it difficult to develop artificial intelligence-based detection systems. Hence, synthetic data generation has emerged as a promising solution to mitigate data scarcity while preserving patient privacy. This study evaluates the feasibility of using synthetic MRI data as an alternative training resource and compares real, synthetic, and combined data for training scenarios under a unified experimental framework. Moreover, synthetic data generation is considered not only as a mechanism for performance enhancement but also as a potential approach for supporting privacy-sensitive medical imaging applications where access to real patient data may be restricted. In this paper, three experimental scenarios were considered: real MR images only, GAN-generated synthetic MR images only, and a hybrid dataset combining both. Transfer learning models were used and their classification success was evaluated using various metrics. The findings show that models perform better with synthetic images alone, while in some models, using original and synthetic data together can have a positive impact on performance. These results demonstrate that GAN-generated synthetic images can provide meaningful data diversity and may serve as an alternative training resource in medical image classification tasks, particularly in scenarios where access to real data is limited.

Anahtar Kelimeler

• Tumor Detection
• Transfer Learning
• Data Privacy
• GAN

Beyin Tümörü Sınıflandırması için Alternatif Bir Eğitim Kaynağı Olarak Sentetik MRI: GAN Tabanlı Bir Çalışma

Öz

Manyetik rezonans (MR) görüntüleme verilerinden beyin tümörlerinin doğru sınıflandırılması, klinik karar destek sistemleri için büyük önem taşımaktadır. Bununla birlikte, MR verilerinde karşılaşılan mahremiyet sorunları ve veri toplama süreçlerinin yüksek maliyeti, yapay zekâ tabanlı tespit sistemlerinin geliştirilmesini zorlaştırmaktadır. Bu bağlamda, sentetik veri üretimi, hasta mahremiyetini korurken veri kıtlığını hafifletmek için umut vadeden bir çözüm olarak ortaya çıkmıştır. Bu çalışma, alternatif bir eğitim kaynağı olarak sentetik MR verilerinin kullanılabilirliğini değerlendirmekte ve gerçek, sentetik ve birleştirilmiş eğitim senaryolarını birleşik bir deneysel çerçeve altında karşılaştırmaktadır. Ayrıca, sentetik veri üretimi yalnızca bir performans artırma mekanizması olarak değil, aynı zamanda gerçek hasta verilerine erişimin kısıtlanabileceği mahremiyet açısından hassas tıbbi görüntüleme uygulamalarını desteklemek için potansiyel bir yaklaşım olarak da ele alınmaktadır. Bu makalede üç deneysel senaryo ele alınmıştır: yalnızca gerçek MR görüntüleri, yalnızca GAN tarafından üretilen sentetik MR görüntüleri ve her ikisini birleştiren hibrit bir veri kümesi. Transfer öğrenme modelleri eğitilmiş ve sınıflandırma başarıları çeşitli ölçütler kullanılarak değerlendirilmiştir. Bulgular, modellerin yalnızca sentetik görüntülerle daha iyi performans gösterdiğini, bazı modellerde ise orijinal ve sentetik verilerin birlikte kullanılmasının performansı olumlu etkileyebileceğini göstermektedir. Bu sonuçlar, GAN tarafından üretilen sentetik görüntülerin anlamlı veri çeşitliliği sağlayabileceğini ve özellikle gerçek verilere erişimin sınırlı olduğu senaryolarda tıbbi görüntü sınıflandırma görevlerinde alternatif bir eğitim kaynağı olarak kullanılabileceğini göstermektedir.

Anahtar Kelimeler

• Tümör tespiti
• Transfer Öğrenme
• Veri Mahremiyeti
• GAN

Kaynakça

1. N. A. O. Bush, S. M. Chang, and M. S. Berger, "Current and future strategies for treatment of glioma," Neurosurgical Review, vol. 40, no. 1, pp. 1-14, 2017/01/01 2017.
2. G. A. Amran et al., "Brain tumor classification and detection using hybrid deep tumor network," Electronics, vol. 11, no. 21, p. 3457, 2022.
3. V. Dutoit, D. Migliorini, P.-Y. Dietrich, and P. R. Walker, "Immunotherapy of malignant tumors in the brain: how different from other sites?," Frontiers in oncology, vol. 6, p. 256, 2016.
4. O. Ronneberger, P. Fischer, and T. Brox, "U-net: Convolutional networks for biomedical image segmentation," in Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18, 2015, pp. 234-241: Springer.
5. F. Milletari, N. Navab, and S.-A. Ahmadi, "V-net: Fully convolutional neural networks for volumetric medical image segmentation," in 2016 fourth international conference on 3D vision (3DV), 2016, pp. 565-571: IEEE.
6. A. R. Benaim et al., "Analyzing medical research results based on synthetic data and their relation to real data results: systematic comparison from five observational studies," JMIR medical informatics, vol. 8, no. 2, p. e16492, 2020.
7. C. Ngufor, H. Van Houten, B. S. Caffo, N. D. Shah, and R. G. McCoy, "Mixed effect machine learning: A framework for predicting longitudinal change in hemoglobin A1c," Journal of biomedical informatics, vol. 89, pp. 56-67, 2019.
8. T. Laderas, N. Vasilevsky, B. Pederson, M. Haendel, S. McWeeney, and D. A. Dorr, "Teaching data science fundamentals through realistic synthetic clinical cardiovascular data," BioRxiv, p. 232611, 2017.

9. I. Goodfellow et al., "Generative adversarial nets," Advances in neural information processing systems, vol. 27, 2014.
10. D. Hennessy, C. Sanmartin, S. Eftekhary, L. Plager, J. Jones, and K. Onate, "Creating a synthetic database for use in microsimulation models to investigate alternative health care financing strategies in Canada," Int J. Microsimul, vol. 8, pp. 41-74, 2015.
11. M. Giuffrè and D. L. Shung, "Harnessing the power of synthetic data in healthcare: innovation, application, and privacy," NPJ digital medicine, vol. 6, no. 1, p. 186, 2023.
12. D. Rankin, M. Black, R. Bond, J. Wallace, M. Mulvenna, and G. Epelde, "Reliability of supervised machine learning using synthetic data in health care: Model to preserve privacy for data sharing," JMIR medical informatics, vol. 8, no. 7, p. e18910, 2020.
13. J. Jordon et al., "Synthetic Data--what, why and how?," arXiv preprint arXiv:2205.03257, 2022.
14. M. Prastawa, E. Bullitt, and G. Gerig, "Simulation of brain tumors in MR images for evaluation of segmentation efficacy," Medical image analysis, vol. 13, no. 2, pp. 297-311, 2009.
15. C. Han et al., "GAN-based synthetic brain MR image generation," in 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018), 2018, pp. 734-738: IEEE.
16. A. Radford, L. Metz, and S. Chintala, "Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv 2015," arXiv preprint arXiv:1511.06434, 2015.
17. M. Arjovsky, S. Chintala, and L. Bottou, "Wasserstein generative adversarial networks," in International conference on machine learning, 2017, pp. 214-223: PMLR.
18. T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen, "Improved techniques for training gans," Advances in neural information processing systems, vol. 29, 2016.
19. S. A. Güven, Use of generative adversarial networks in medical image synthesis and segmentation, PhD Thesis, İnönü University, Turkey, 2022.
20. M. Raja and S. Vijayachitra, "A hybrid approach to segment and detect brain abnormalities from MRI scan," Expert Systems with Applications, vol. 216, p. 119435, 2023.
21. K. Kazuhiro et al., "Generative adversarial networks for the creation of realistic artificial brain magnetic resonance images," Tomography, vol. 4, no. 4, p. 159, 2018.
22. E. Güvenç, G. Çetin, and M. Ersoy, "Generating Synthetic Images from Real MR Images Using Deep Learning Methods," Gazi Journal of Engineering Sciences, vol. 9, no. 4, pp. 230-239, 2023.
23. H. H. N. Alrashedy, A. F. Almansour, D. M. Ibrahim, and M. A. A. Hammoudeh, "BrainGAN: brain MRI image generation and classification framework using GAN architectures and CNN models," Sensors, vol. 22, no. 11, p. 4297, 2022.
24. R. Mehrotra, M. Ansari, R. Agrawal, and R. Anand, "A transfer learning approach for AI-based classification of brain tumors," Machine Learning with Applications, vol. 2, p. 100003, 2020.
25. Y. Jiang, H. Chen, M. Loew, and H. Ko, "COVID-19 CT image synthesis with a conditional generative adversarial network," IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 2, pp. 441-452, 2020.
26. H. P. Das et al., "Conditional synthetic data generation for robust machine learning applications with limited pandemic data," in Proceedings of the AAAI Conference on Artificial Intelligence, 2022, vol. 36, no. 11, pp. 11792-11800.
27. B. Loong, A. M. Zaslavsky, Y. He, and D. P. Harrington, "Disclosure control using partially synthetic data for large‐scale health surveys, with applications to CanCORS," Statistics in medicine, vol. 32, no. 24, pp. 4139-4161, 2013.
28. H. M. T. Khushi, T. Masood, A. Jaffar, and S. Akram, "A Novel Approach to Classify Brain Tumor with an Effective Transfer Learning based Deep Learning Model," Brazilian Archives of Biology and Technology, vol. 67, p. e24231137, 2024.
29. O. Bouguerra, B. Attallah, and Y. Brik, "MRI-based brain tumor ensemble classification using two stage score level fusion and CNN models," Egyptian Informatics Journal, vol. 28, p. 100565, 2024.
30. M. Ghaffari, A. Sowmya, and R. Oliver, "Automated brain tumor segmentation using multimodal brain scans: a survey based on models submitted to the BraTS 2012–2018 challenges," IEEE reviews in biomedical engineering, vol. 13, pp. 156-168, 2019.
31. B. H. Menze et al., "The multimodal brain tumor image segmentation benchmark (BRATS)," IEEE transactions on medical imaging, vol. 34, no. 10, pp. 1993-2024, 2014.
32. D. Zikic, Y. Ioannou, M. Brown, and A. Criminisi, "Segmentation of brain tumor tissues with convolutional neural networks," Proceedings MICCAI-BRATS, vol. 36, no. 2014, pp. 36-39, 2014.
33. M. Havaei et al., "Brain tumor segmentation with deep neural networks," Medical image analysis, vol. 35, pp. 18-31, 2017.
34. S. Bakas et al., "Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge," arXiv preprint arXiv:1811.02629, 2018.
35. F. Isensee, P. Kickingereder, W. Wick, M. Bendszus, and K. H. Maier-Hein, "Brain tumor segmentation and radiomics survival prediction: Contribution to the brats 2017 challenge," in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: Third International Workshop, BrainLes 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Revised Selected Papers 3, 2018, pp. 287-297: Springer.
36. K. Kamnitsas et al., "DeepMedic for brain tumor segmentation," in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: Second International Workshop, BrainLes 2016, with the Challenges on BRATS, ISLES and mTOP 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised Selected Papers 2, 2016, pp. 138-149: Springer.
37. X. Zhao, Y. Wu, G. Song, Z. Li, Y. Fan, and Y. Zhang, "Brain tumor segmentation using a fully convolutional neural network with conditional random fields," in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: Second International Workshop, BrainLes 2016, with the Challenges on BRATS, ISLES and mTOP 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised Selected Papers 2, 2016, pp. 75-87: Springer.
38. L. Weninger, O. Rippel, S. Koppers, and D. Merhof, "Segmentation of brain tumors and patient survival prediction: Methods for the brats 2018 challenge," in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part II 4, 2019, pp. 3-12: Springer.
39. M. I. Sharif, J. P. Li, M. A. Khan, and M. A. Saleem, "Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images," Pattern Recognition Letters, vol. 129, pp. 181-189, 2020.
40. J. Nalepa, M. Marcinkiewicz, and M. Kawulok, "Data augmentation for brain-tumor segmentation: a review," Frontiers in computational neuroscience, vol. 13, p. 83, 2019.
41. Z. Jiang, C. Ding, M. Liu, and D. Tao, "Two-stage cascaded u-net: 1st place solution to brats challenge 2019 segmentation task," in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 5th International Workshop, BrainLes 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Revised Selected Papers, Part I 5, 2020, pp. 231-241: Springer.
42. H.-M. Chen et al., "Comparison of Multispectral Image‐Processing Methods for Brain Tissue Classification in BrainWeb Synthetic Data and Real MR Images," BioMed Research International, vol. 2021, no. 1, p. 9820145, 2021.
43. R. Anitha and D. Siva Sundhara Raja, "Development of computer‐aided approach for brain tumor detection using random forest classifier," International Journal of Imaging Systems and Technology, vol. 28, no. 1, pp. 48-53, 2018.
44. M. Sharif, U. Tanvir, E. U. Munir, M. A. Khan, and M. Yasmin, "Brain tumor segmentation and classification by improved binomial thresholding and multi-features selection," Journal of ambient intelligence and humanized computing, pp. 1-20, 2024.
45. S. Basheera and M. Satya Sai Ram, "Classification of brain tumors using deep features extracted using CNN," in Journal of Physics: Conference Series, 2019, vol. 1172, p. 012016: IOP Publishing.
46. Y. K. Chandra and A. Agrawal, "Deep learning based brain tumor segmentation and classification using MRI images," in 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT), 2022, pp. 1248-1254: IEEE.
47. M. S. Fuad, C. Anam, K. Adi, and G. Dougherty, "Comparison of two convolutional neural network models for automated classification of brain cancer types," in AIP Conference Proceedings, 2021, vol. 2346, no. 1: AIP Publishing.
48. S. Kadry, R. Damaševičius, D. Taniar, V. Rajinikanth, and I. A. Lawal, "Extraction of tumour in breast MRI using joint thresholding and segmentation–A study," in 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII), 2021, pp. 1-5: IEEE.
49. J. Amin, M. Sharif, M. Yasmin, and S. L. Fernandes, "A distinctive approach in brain tumor detection and classification using MRI," Pattern Recognition Letters, vol. 139, pp. 118-127, 2020.
50. A. Kujur, Z. Raza, A. A. Khan, and C. Wechtaisong, "Data complexity based evaluation of the model dependence of brain MRI images for classification of brain tumor and Alzheimer’s disease," IEEE Access, vol. 10, pp. 112117-112133, 2022.
51. J. Acquarelli et al., "Convolutional neural networks to predict brain tumor grades and Alzheimer’s disease with MR spectroscopic imaging data," Plos one, vol. 17, no. 8, p. e0268881, 2022.
52. A. Ashraf, Z. Qingjie, W. H. K. Bangyal, and M. Iqbal, "Analysis of brain imaging data for the detection of early age autism spectrum disorder using transfer learning approaches for internet of things," IEEE Transactions on Consumer Electronics, vol. 70, no. 1, pp. 4478-4489, 2023.
53. X. Li, Y. Gu, N. Dvornek, L. H. Staib, P. Ventola, and J. S. Duncan, "Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results," Medical image analysis, vol. 65, p. 101765, 2020.
54. Z. N. K. Swati et al., "Brain tumor classification for MR images using transfer learning and fine-tuning," Computerized Medical Imaging and Graphics, vol. 75, pp. 34-46, 2019.
55. M. M. Zahoor et al., "A new deep hybrid boosted and ensemble learning-based brain tumor analysis using MRI," Sensors, vol. 22, no. 7, p. 2726, 2022.
56. Y. Gusev, K. Bhuvaneshwar, L. Song, J.-C. Zenklusen, H. Fine, and S. Madhavan, "The REMBRANDT study, a large collection of genomic data from brain cancer patients," Scientific data, vol. 5, no. 1, pp. 1-9, 2018.
57. L. Shen and T. Anderson, "Multimodal brain MRI tumor segmentation via convolutional neural networks," vol, vol. 18, pp. 2014-2015, 2017.
58. K. Özdem et al., "A ga-based cnn model for brain tumor classification," in 2022 7th International Conference on Computer Science and Engineering (UBMK), 2022, pp. 418-423: IEEE.
59. A. G. Eker, G. K. Erdem, and N. Duru, "Categorical and Binary Brain Tumor Classification Using Transfer Learning Techniques," Sivas Cumhuriyet University Journal of the Faculty of Engineering, vol. 1, no. 1, pp. 11-16, 2023.
60. H. E. Kim, A. Cosa-Linan, N. Santhanam, M. Jannesari, M. E. Maros, and T. Ganslandt, "Transfer learning for medical image classification: a literature review," BMC medical imaging, vol. 22, no. 1, p. 69, 2022.
61. G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, "Densely connected convolutional networks," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 4700-4708.
62. F. Chollet, "Xception: Deep learning with depthwise separable convolutions," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 1251-1258.
63. K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
64. F. Doğan and İ. Türkoğlu, "Derin öğrenme modelleri ve uygulama alanlarına ilişkin bir derleme," Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, vol. 10, no. 2, pp. 409-445, 2019.
65. T. S. Azzahra, J. J. Cerelia, F. A. L. Nugraha, and A. A. Pravitasari, "MRI-based brain tumor classification using inception resnet V2," Enthusiastic: International Journal of Applied Statistics and Data Science, pp. 163-175, 2023.
66. S. Mascarenhas and M. Agarwal, "A comparison between VGG16, VGG19 and ResNet50 architecture frameworks for Image Classification," in 2021 International conference on disruptive technologies for multi-disciplinary research and applications (CENTCON), 2021, vol. 1, pp. 96-99: IEEE.
67. A. Younis et al., "Abnormal brain tumors classification using resnet50 and its comprehensive evaluation," IEEE Access, vol. 12, pp. 78843-78853, 2024.
68. R. Pillai, A. Sharma, N. Sharma, and R. Gupta, "Brain tumor classification using VGG 16, ResNet50, and inception V3 transfer learning models," in 2023 2nd International Conference for Innovation in Technology (INOCON), 2023, pp. 1-5: IEEE.
69. D. S. Wankhede, C. J. Shelke, V. K. Shrivastava, R. Achary, and S. N. Mohanty, "Brain Tumor Detection and Classification Using Adjusted InceptionV3, AlexNet, VGG16, VGG19 with ResNet50-152 CNN Model," EAI Endorsed Transactions on Pervasive Health & Technology, vol. 10, no. 1, 2024.
70. M. T. R. Shawon, G. S. Shibli, F. Ahmed, and S. K. S. Joy, "Explainable cost-sensitive deep neural networks for brain tumor detection from brain mri images considering data imbalance," Multimedia Tools and Applications, pp. 1-28, 2025.
71. M. Celik and O. Inik, "Development of hybrid models based on deep learning and optimized machine learning algorithms for brain tumor Multi-Classification," Expert Systems with Applications, vol. 238, p. 122159, 2024.
72. S. Moldovanu, G. Tăbăcaru, and M. Barbu, "Convolutional neural Network–Machine learning model: hybrid model for meningioma tumour and healthy brain classification," Journal of Imaging, vol. 10, no. 9, p. 235, 2024.
73. A. F. Al Bataineh et al., "Enhanced magnetic resonance imaging-based brain tumor classification with a hybrid Swin Transformer and ResNet50V2 model," Applied Sciences, vol. 14, no. 22, p. 10154, 2024.
74. K. Mahmut, Bilgisayar Bilimlerinde Güncel Konular ve Yeni Nesil Teknolojiler. Akademisyen Kitabevi, 2022.
75. A. Dash, J. Ye, and G. Wang, "A review of generative adversarial networks (GANs) and its applications in a wide variety of disciplines: from medical to remote sensing," IEEE Access, 2023.
76. G. Degottex and M. Gales, "A Spectrally Weighted Mixture of Least Square Error and Wasserstein Discriminator Loss for Generative SPSS," in 2018 IEEE Spoken Language Technology Workshop (SLT), 2018, pp. 603-609: IEEE.
77. Y. Canbay, S. Adsiz, and P. Canbay, “Privacy-preserving transfer learning framework for kidney disease detection,” Applied Sciences, vol. 14, no. 19, Art. no. 8629, 2024.