Federated and GAN-Augmented Deep Learning Frameworks for Brain Tumor Diagnosis: A Comprehensive Survey
Abstract
Deep learning has transformed brain tumor detection and segmentation, yet clinical translation is impeded by data scarcity, class imbalance, heterogeneous imaging protocols, interpretability gaps and heavy computational demands This review brings together recent developments and highlights Generative Adversarial Networks (GANs) and Federated Learning as central elements in practical applications. GANs expand limited datasets with high‑fidelity synthetic images that improve diversity, robustness and resolution for training, while federated learning enables privacy‑preserving, multi‑institutional model development that enhances generalization without exchanging raw patient data. We contrast GANs with VAEs and diffusion models, highlighting trade-offs among fidelity, interpretability, stability and efficiency, and examine hybrid CNN-transformer architectures, multimodal fusion, radiomics and optimization strategies that bolster performance. Key future directions include hybrid generative frameworks, explainable AI for transparent grading, fine‑grained tumor staging, multimodal integration, scalable architectures for resource‑limited settings and standardized benchmarks for reproducibility. By situating GANs and federated learning within an ethical and reproducible framework, AI systems may be developed to support clinical reliability, safeguard privacy and prioritize patient needs by helping to connect research with practical application.
- Referencias
- Cómo citar
- Del mismo autor
- Métricas
Abdulqader, M. M., & Abdulazeez, A. M. (2025). A comparative study of generative adversarial networks in medical image processing. Eng, 6(11), 291. https://doi.org/10.3390/eng6110291
Abirami, S., & Prasanna Venkatesan, G. K. D. (2022). Deep learning and Spark architecture-based intelligent brain tumor MRI image severity classification. Biomedical Signal Processing and Control, 76, 103644. https://doi.org/10.1016/j.bspc.2022.103644
Agarwal, M., Rani, G., Kumar, A., Kumar, P. K., Manikandan, R., & Gandomi, A. H. (2024). Deep learning for enhanced brain tumor detection and classification. Results in Engineering, 22, 102117. https://doi.org/10.1016/j.rineng.2024.102117
Ahmad, B., Sun, J., You, Q., Palade, V., & Mao, Z. (2022). Brain tumor classification using a combination of variational autoencoders and generative adversarial networks. Biomedicines, 10(2), 223. https://doi.org/10.3390/biomedicines10020223
Alemu, B. S., Feisso, S., Mohammed, E. A., & Salau, A. O. (2023). Magnetic resonance imaging-based brain tumor image classification performance enhancement. Scientific African, 22, e01963. https://doi.org/10.1016/j.sciaf.2023.e01963
Al-Jammas, M. H., Al-Sabawi, E. A., Yassin, A. M., & Abdulrazzaq, A. H. (2024). Brain tumors recognition based on deep learning. e-Prime - Advances in Electrical Engineering, Electronics and Energy, 8, 100500. https://doi.org/10.1016/j.prime.2024.100500
Almuhaimeed, A., Bilal, A., Alzahrani, A., Alrashidi, M., Alghamdi, M., & Sarwar, R. (2025). Brain tumor classification using GAN-augmented data with autoencoders and Swin transformers. Frontiers in Medicine, 12, 1635796. https://doi.org/10.3389/fmed.2025.1635796
Al-Saleh, A., Tejani, G. G., Mishra, S., et al. (2025). A federated learning-based privacy-preserving image processing framework for brain tumor detection from CT scans. Scientific Reports, 15, 23578. https://doi.org/10.1038/s41598-025-07807-8
Amin, J., Sharif, M., Raza, M., Saba, T., & Anjum, M. A. (2019). Brain tumor detection using statistical and machine learning method. Computer Methods and Programs in Biomedicine, 177, 69–79. https://doi.org/10.1016/j.cmpb.2019.05.015
Anantharajan, S., Gunasekaran, S., Subramanian, T., & Venkatesh, R. (2024). MRI brain tumor detection using deep learning and machine learning approaches. Measurement: Sensors, 31, 101026. https://doi.org/10.1016/j.measen.2024.101026
Anaraki, A. K., Ayati, M., & Kazemi, F. (2019). Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms. Biocybernetics and Biomedical Engineering, 39(1), 63–74. https://doi.org/10.1016/j.bbe.2018.10.004
Appiah, R., Pulletikurthi, V., Esquivel-Puentes, H. A., Cabrera, C., Hasan, N. I., Dharmarathne, S., Gomez, L. J., & Castillo, L. (2024). Brain tumor detection using proper orthogonal decomposition integrated with deep learning networks. Computer Methods and Programs in Biomedicine, 250, 108167. https://doi.org/10.1016/j.cmpb.2024.108167
Behnam, A. (2025). VGDM: Vision-guided diffusion model for brain tumor detection and segmentation [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2510.02086
Bhuvaneswari Ramakrishnan, A., Sridevi, M., Vasudevan, S. K., Manikandan, R., & Gandomi, A. H. (2024). Optimizing brain tumor classification with hybrid CNN architecture: Balancing accuracy and efficiency through oneAPI optimization. Informatics in Medicine Unlocked, 44, 101436. https://doi.org/10.1016/j.imu.2023.101436
Çelik, M., & İnik, Ö. (2024). Development of hybrid models based on deep learning and optimized machine learning algorithms for brain tumor multi-classification. Expert Systems with Applications, 238, 122159. https://doi.org/10.1016/j.eswa.2023.122159
Çetiner, H., & Metlek, S. (2023). DenseUNet+: A novel hybrid segmentation approach based on multimodality images for brain tumor segmentation. Journal of King Saud University - Computer and Information Sciences, 35(8), 101663. https://doi.org/10.1016/j.jksuci.2023.101663
Chandni, Sachdeva, M., & Kushwaha, A. K. S. (2024). IRNetv: A deep learning framework for automated brain tumor diagnosis. Biomedical Signal Processing and Control, 87, 105459. https://doi.org/10.1016/j.bspc.2023.105459
Cheng, D., Gao, X., Mao, Y., Xiao, B., You, P., Gai, J., Zhu, M., Kang, J., Zhao, F., & Mao, N. (2023). Brain tumor feature extraction and edge enhancement algorithm based on U-Net network. Heliyon, 9(11), e22536. https://doi.org/10.1016/j.heliyon.2023.e22536
Dikande Simo, A. M., Tchagna Kouanou, A., Monthe, V., Kameni Nana, M., & Moffo Lonla, B. (2024). Introducing a deep learning method for brain tumor classification using MRI data towards better performance. Informatics in Medicine Unlocked, 44, 101423. https://doi.org/10.1016/j.imu.2023.101423
Elagamy, M., Selim, H., & Elkaffas, S. M. (2023). Enhanced brain tumor detection and segmentation using identity generative adversarial network and attention residual U-Net. In 2023 33rd International Conference on Computer Theory and Applications (ICCTA) (pp. 272–277). IEEE. https://doi.org/10.1109/ICCTA60978.2023.10969194
Farajzadeh, N., Sadeghzadeh, N., & Hashemzadeh, M. (2023). Brain tumor segmentation and classification on MRI via deep hybrid representation learning. Expert Systems with Applications, 224, 119963. https://doi.org/10.1016/j.eswa.2023.119963
Fernando, K. R. M., & Tsokos, C. P. (2023). Deep and statistical learning in biomedical imaging: State of the art in 3D MRI brain tumor segmentation. Information Fusion, 92, 450–465. https://doi.org/10.1016/j.inffus.2022.12.013
Gab Allah, A. M., Sarhan, A. M., & Elshennawy, N. M. (2023). Edge U-Net: Brain tumor segmentation using MRI based on deep U-Net model with boundary information. Expert Systems with Applications, 213(Part A), 118833. https://doi.org/10.1016/j.eswa.2022.118833
Gupta, B. B., Gaurav, A., & Arya, V. (2024). Deep CNN based brain tumor detection in intelligent systems. International Journal of Intelligent Networks, 5, 30–37. https://doi.org/10.1016/j.ijin.2023.12.001
Gurunathan, A., & Krishnan, B. (2020). Detection and diagnosis of brain tumors using deep learning convolutional neural networks. International Journal of Imaging Systems and Technology, 31(3), 1537–1552. https://doi.org/10.1002/ima.22532
Irmak, E. (2021). Multi-classification of brain tumor MRI images using deep convolutional neural network with fully optimized framework. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 45, 1015–1036. https://doi.org/10.1007/s40998-021-00426-9
Islam, M. N., Azam, M. S., Islam, M. S., Kanchan, M. H., Parvez, A. H. M. S., & Islam, M. M. (2024). An improved deep learning-based hybrid model with ensemble techniques for brain tumor detection from MRI image. Informatics in Medicine Unlocked, 47, 101483. https://doi.org/10.1016/j.imu.2024.101483
Khan, A. H., Abbas, S., Khan, M. A., Farooq, U., Khan, W. A., Siddiqui, S. Y., & Ahmad, A. (2022). Intelligent model for brain tumor identification using deep learning. Applied Computational Intelligence and Soft Computing, 2022, 8104054. https://doi.org/10.1155/2022/8104054
Kidder, B. L. (2024). Advanced image generation for cancer using diffusion models. Biology Methods and Protocols, 9(1), bpae062. https://doi.org/10.1093/biomethods/bpae062
Kordnoori, S., Sabeti, M., Shakoor, M. H., & Moradi, E. (2024). Deep multi-task learning structure for segmentation and classification of supratentorial brain tumors in MR images. Interdisciplinary Neurosurgery, 36, 101931. https://doi.org/10.1016/j.inat.2023.101931
Kumar, G. M., & Parthasarathy, E. (2023). Development of an enhanced U-Net model for brain tumor segmentation with optimized architecture. Biomedical Signal Processing and Control, 81, 104427. https://doi.org/10.1016/j.bspc.2022.104427
Kumar, P. S., Sakthivel, V. P., Raju, M., & Sathya, P. D. (2023). Brain tumor segmentation of the FLAIR MRI images using novel ResUnet. Biomedical Signal Processing and Control, 82, 104586. https://doi.org/10.1016/j.bspc.2023.104586
Lamba, K., Rani, S., Anand, M., & Maguluri, L. P. (2024). An integrated deep learning and supervised learning approach for early detection of brain tumor using magnetic resonance imaging. Healthcare Analytics, 5, 100336. https://doi.org/10.1016/j.health.2024.100336
Manasa, B., Lakshmi Sanjana, K. D. V., Gowtham, K., Tejaswini, G., & Akila, V. (2026). A two-stage generative model for unsupervised brain tumor detection using CycleGAN and diffusion models. Iconic Research and Engineering Journals, 9(9), 816–825. https://doi.org/10.64388/IREV9I9-1715054
Mehnatkesh, H., Jalali, S. M. J., Khosravi, A., & Nahavandi, S. (2023). An intelligent-driven deep residual learning framework for brain tumor classification using MRI images. Expert Systems with Applications, 213, 119087. https://doi.org/10.1016/j.eswa.2022.119087
Mehrotra, R., Ansari, M. A., Agrawal, R., & Anand, R. S. (2020). A transfer learning approach for AI-based classification of brain tumors. Machine Learning with Applications, 2, 100003. https://doi.org/10.1016/j.mlwa.2020.100003
Mirowski, P., & Fabijańska, A. (2026). Diffusion model-based synthesis of brain images for data augmentation. Biomedical Signal Processing and Control, 113, 108940. https://doi.org/10.1016/j.bspc.2025.108940
Moitra, D., & Mandal, R. K. (2022). Classification of malignant tumors by a non-sequential recurrent ensemble of deep neural network model. Multimedia Tools and Applications, 81(7), 10279–10297. https://doi.org/10.1007/s11042-022-12229-z
Monisha, S., & Rahman, R. (2025). Brain tumor detection in MRI based on federated learning with YOLOv11 [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2503.04087
Nie, Z., Yang, J., Li, C., Wang, Y., & Tang, J. (2025). DiffBTS: A lightweight diffusion model for 3D multi-modal brain tumor segmentation. Sensors, 25(10), 2985. https://doi.org/10.3390/s25102985
Nizamani, A. H., Chen, Z., Nizamani, A. A., & Bhatti, U. A. (2023). Advance brain tumor segmentation using feature fusion methods with deep U-Net model with CNN for MRI data. Journal of King Saud University - Computer and Information Sciences, 35(9), 101793. https://doi.org/10.1016/j.jksuci.2023.101793
Raghuram, B., & Hanumanthu, B. (2023). Brain tumor image identification and classification on the internet of medical things using deep learning. Measurement: Sensors, 30, 100905. https://doi.org/10.1016/j.measen.2023.100905
Ramesh, S. M., & Gururaj, C. (2026). Advanced MRI-based deep learning for brain tumors: A five-year review of oncology-radiology-AI synergy. International Journal of Reconfigurable and Embedded Systems, 15(1), 214–223. https://doi.org/10.11591/ijres.v15.i1.pp214-223
Ranjbarzadeh, R., Zarbakhsh, P., Caputo, A., Tirkolaee, E. B., & Bendechache, M. (2024). Brain tumor segmentation based on optimized convolutional neural network and improved chimp optimization algorithm. Computers in Biology and Medicine, 168, 107723. https://doi.org/10.1016/j.compbiomed.2023.107723
Rastogi, D., Johri, P., Tiwari, V., & Elngar, A. A. (2024). Multi-class classification of brain tumour magnetic resonance images using multi-branch network with inception block and five-fold cross-validation deep learning framework. Biomedical Signal Processing and Control, 88, 105602. https://doi.org/10.1016/j.bspc.2023.105602
Ren, W., Bashkandi, A. H., Jahanshahi, J. A., AlHamad, A. Q. M., Javaheri, D., & Mohammadi, M. (2023). Brain tumor diagnosis using a step-by-step methodology based on courtship learning-based water strider algorithm. Biomedical Signal Processing and Control, 83, 104614. https://doi.org/10.1016/j.bspc.2023.104614
Saba, T., Mohamed, A. S., El-Affendi, M., Amin, J., & Sharif, M. (2020). Brain tumor detection using fusion of hand-crafted and deep learning features. Cognitive Systems Research, 59, 221–230. https://doi.org/10.1016/j.cogsys.2019.09.007
Salama, W. M., & Aly, M. H. (2026). Brain tumor segmentation and classification: A CVAE-UNETR-ResNet50-VGG16 hybrid deep learning approach. Alexandria Engineering Journal, 135, 433–449. https://doi.org/10.1016/j.aej.2026.01.003
Sandhiya, B., & Raja, S. K. S. (2024). Deep learning and optimized learning machine for brain tumor classification. Biomedical Signal Processing and Control, 89, 105778. https://doi.org/10.1016/j.bspc.2023.105778
Saraswat, A., & Pandey, N. N. (2026). Hybrid CNN-transformer federated learning for brain tumor segmentation across multi-center MRI datasets. International Journal of Drug Delivery Technology, 16(30s), 278–294. https://doi.org/10.25258/ijddt.16.30s.28
Sasank, V. V. S., & Venkateswarlu, S. (2022). Hybrid deep neural network with adaptive rain optimizer algorithm for multi-grade brain tumor classification of MRI images. Multimedia Tools and Applications, 81, 8021–8057. https://doi.org/10.1007/s11042-022-12106-9
Sharif, M. I., Li, J. P., Khan, M. A., et al. (2024). M3BTCNet: Multi-model brain tumor classification using metaheuristic deep neural network features optimization. Neural Computing and Applications, 36, 95–110. https://doi.org/10.1007/s00521-022-07204-6
Shrividhiya, G., Kashyap, S. N., Srujana, K. S., & Gururaj, C. (2023). Brain tumor detection based on different deep neural networks: A comparison study. In P. Nand, V. Jain, D.-N. Le, J. M. Chatterjee, R. Kannan, & A. S. Verma (Eds.), Deep learning for healthcare services (pp. 63–89). Bentham Science Publishers. https://doi.org/10.2174/9789815080230123020006
Singh, T., Nair, R. R., Babu, T., Wagh, A., Bhosale, A., & Duraisamy, P. (2024). BrainNet: A deep learning approach for brain tumor classification. Procedia Computer Science, 235, 2074–2083. https://doi.org/10.1016/j.procs.2024.04.310
Soh, W. K., Yuen, H. Y., & Rajapakse, J. C. (2023). HUT: Hybrid UNet transformer for brain lesion and tumour segmentation. Heliyon, 9(12), e22412. https://doi.org/10.1016/j.heliyon.2023.e22412
Sultanpure, K. A., Bagade, J., Bangare, S. L., Bangare, M. L., Bamane, K. D., & Patankar, A. J. (2024). Internet of things and deep learning based digital twins for diagnosis of brain tumor by analyzing MRI images. Measurement: Sensors, 33, 101220. https://doi.org/10.1016/j.measen.2024.101220
Tabatabaei, S., Rezaee, K., & Zhu, M. (2023). Attention transformer mechanism and fusion-based deep learning architecture for MRI brain tumor classification system. Biomedical Signal Processing and Control, 86, 105119. https://doi.org/10.1016/j.bspc.2023.105119
Talukder, M. A., Islam, M. M., Uddin, M. A., Akhter, A., Pramanik, M. A. J., Aryal, S., Almoyad, M. A. A., Hasan, K. F., & Moni, M. A. (2023). An efficient deep learning model to categorize brain tumor using reconstruction and fine-tuning. Expert Systems with Applications, 230, 120534. https://doi.org/10.1016/j.eswa.2023.120534
Tripathy, S., Singh, R., & Ray, M. (2023). Automation of brain tumor identification using EfficientNet on magnetic resonance images. Procedia Computer Science, 218, 1551–1560. https://doi.org/10.1016/j.procs.2023.01.133
Ullah, F., Nadeem, M., Abrar, M., Amin, F., Salam, A., & Khan, S. (2023). Enhancing brain tumor segmentation accuracy through scalable federated learning with advanced data privacy and security measures. Mathematics, 11(19), 4189. https://doi.org/10.3390/math11194189
Vankdothu, R., Hameed, M. A., & Fatima, H. (2022). A brain tumor identification and classification using deep learning based on CNN-LSTM method. Computers & Electrical Engineering, 101, 107960. https://doi.org/10.1016/j.compeleceng.2022.107960
Vijayendher, G., Karthikeya, K. S., Madhav, E. J., & Kiranmai, T. S. (2025). Enhancing MRI tumor detection: A survey on image upscaling, GAN-based data augmentation and federated learning in convolutional neural networks. In M. meTuba, S. Akashe, & A. Joshi (Eds.), ICT systems and sustainability: ICT4SD 2025 (Lecture Notes in Networks and Systems, Vol. 1648). Springer. https://doi.org/10.1007/978-3-032-06671-8_32
Wen, J., Li, X., Ye, X., et al. (2025). A highly generalized federated learning algorithm for brain tumor segmentation. Scientific Reports, 15, Article 21053. https://doi.org/10.1038/s41598-025-05297-2
Wu, P., & Shen, J. (2023). Brain tumor diagnosis based on convolutional neural network improved by a new version of political optimizer. Biomedical Signal Processing and Control, 85, 104853. https://doi.org/10.1016/j.bspc.2023.104853
Yaqub, M., Jinchao, F., Ahmed, S., Mehmood, A., Chuhan, I. S., Manan, M. A., & Pathan, M. S. (2023). DeepLabV3, IBCO-based ALCResNet: A fully automated classification and grading system for brain tumor. Alexandria Engineering Journal, 76, 609–627. https://doi.org/10.1016/j.aej.2023.06.062
Yu, B., Xu, C., Yin, Q., et al. (2025). Conditional diffusion model for high-accuracy brain tumor segmentation in MRI images. Scientific Reports, 15, 41326. https://doi.org/10.1038/s41598-025-25137-7
Zuo, C., Xue, J., & Yuan, C. (2025). Multichannel fusion diffusion models for brain tumor MRI data augmentation. Scientific Reports, 15, 22459. https://doi.org/10.1038/s41598-025-06529-1