Abstract：Brain tumors are malignant diseases caused by abnormal proliferation of brain cells, seriously threatening the life of patients. Magnetic resonance imaging (MRI), as a non-invasive and clear diagnostic tool, is widely used in the diagnosis of brain tumors. In recent years, deep learning technology has made breakthrough progress in the field of medical image analysis, providing new approaches for the diagnosis and lesion localization of brain tumors. This article reviewed the application progress of deep learning in MRI images of brain tumors, mainly elaborating from three aspects: multi-scale feature extraction, lesion segmentation and localization, and classification and grading. The application of generative models in alleviating the scarcity of MRI data was summarized, and the advantages of federated collaborative learning in multi-institution and multi-data fusion were introduced as well. This paper pointed out that deep learning is still faceing challenges such as insufficient model interpretability and scarce data in the analysis of brain tumor images, at the same time, its future development directions were discussed.
姜良, 马星民, 王华. 基于深度学习的脑肿瘤MRI图像诊断研究进展[J]. 中国生物医学工程学报, 2026, 45(1): 79-86.
Jiang Liang, Ma Xingmin, Wang Hua. Research Progress of Brain Tumor MRI Image Diagnosis Based on Deep Learning. Chinese Journal of Biomedical Engineering, 2026, 45(1): 79-86.
[1] 李光宇,杨锋,张智悦,等. 深度残差网络在脑肿瘤MRI分类上的研究进展 [J]. 磁共振成像, 2025, 16(3): 143-149,161. [2] Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries [J]. CA: Cancer Journal for Clinicians, 2021, 71(3): 209-249. [3] Van den Bent MJ, Geurts M, French PJ, et al. Primary brain tumours in adults [J]. The Lancet, 2023, 402(10412): 1564-1579. [4] Salari N, Ghasemi H, Fatahian R, et al. The global prevalence of primary central nervous system tumors: a systematic review and meta-analysis [J]. European Journal of Medical Research, 2023, 28: 39. [5] Kumar KK, Dinesh PM, Rayavel P, et al. Brain tumor identification using data augmentation and transfer learning approach [J]. Computer Systems Science & Engineering, 2023, 46(2): 1845-1861. [6] Kumar A. Study and analysis of different segmentation methods for brain tumor MRI application [J]. Multimedia Tools and Applications, 2023, 82(5): 7117-7139. [7] Ranjbarzadeh R, Caputo A, Tirkolaee EB, et al. Brain tumor segmentation of MRI images: a comprehensive review on the application of artificial intelligence tools [J]. Computers in Biology and Medicine, 2023, 152: 106405. [8] Martucci M, Russo R, Schimperna F, et al. Magnetic resonance imaging of primary adult brain tumors: state of the art and future perspectives [J]. Biomedicines, 2023, 11(2): 364. [9] Balasubramanian S, Mandala J, Rao TVM, et al. RF-ShCNN: a combination of two deep models for tumor detection in brain using MRI [J]. Biomedical Signal Processing and Control, 2024, 88: 105656. [10] Zhu Zhiqin, Wang Ziyu, Qi Guanqiu, et al. Brain tumor segmentation in MRI with multi-modality spatial information enhancement and boundary shape correction [J]. Pattern Recognition, 2024, 153: 110553. [11] Santana LS, Diniz JBC, Gasparri LMG, et al. Application of machine learning for classification of brain tumors: a systematic review and meta-analysis [J]. World Neurosurgery, 2024, 186: 204-218. [12] Haq AU, Li JP, Kumar R, et al. MCNN: a multi-level CNN model for the classification of brain tumors in IoT-healthcare system [J]. Journal of Ambient Intelligence and Humanized Computing, 2023, 14(5): 4695-4706. [13] Sahoo AK, Parida P, Muralibabu K, et al. Efficient simultaneous segmentation and classification of brain tumors from MRI scans using deep learning [J]. Biocybernetics and Biomedical Engineering, 2023, 43(3): 616-633. [14] Rahman T, Islam MS. MRI brain tumor detection and classification using parallel deep convolutional neural networks [J]. Measurement: Sensors, 2023, 26: 100694. [15] Asif S, Ying Rongbiao, Wang Enyu. Swarm intelligence optimization-based fusion of ConvMixer-enhanced deep neural networks for brain tumor detection [J]. Swarm and Evolutionary Computation, 2025, 98: 102121. [16] Sun Jindong, Peng Yanjun, Guo Yanfei, et al. Segmentation of the multimodal brain tumor image used the multi-pathway architecture method based on 3D FCN [J]. Neurocomputing, 2021, 423: 34-45. [17] Dutta TK, Nayak DR, Zhang Yudong. Arm-net: attention-guided residual multiscale CNN for multiclass brain tumor classification using MR images [J]. Biomedical Signal Processing and Control, 2024, 87: 105421. [18] Sobhaninia Z, Karimi N, Khadivi P, et al. Brain tumor segmentation by cascaded multiscale multitask learning framework based on feature aggregation [J]. Biomedical Signal Processing and Control, 2023, 85: 104834. [19] Wu Peishu, Wang Zidong, Zheng Baixun, et al. AGGN: attention-based glioma grading network with multi-scale feature extraction and multi-modal information fusion [J]. Computers in Biology and Medicine, 2023, 152: 106457. [20] Abd-Ellah MK, Awad AI, Khalaf AAM, et al. A review on brain tumor diagnosis from MRI images: practical implications, key achievements, and lessons learned [J]. Magnetic Resonance Imaging, 2019, 61: 300-318. [21] Ullah Z, Usman M, Jeon M, et al. Cascade multiscale residual attention CNNs with adaptive ROI for automatic brain tumor segmentation [J]. Information sciences, 2022, 608: 1541-1556. [22] 孙康康,陈伟,李奇轩等. 多模态MRI脑肿瘤图像分割方法研究进展 [J]. 磁共振成像, 2023, 14(11): 164-169,176. [23] Sasank VVS, Venkateswarlu S. An automatic tumour growth prediction based segmentation using full resolution convolutional network for brain tumour [J]. Biomedical Signal Processing and Control, 2022, 71: 103090. [24] Vijay S, Guhan T, Srinivasan K, et al. MRI brain tumor segmentation using residual spatial pyramid pooling-powered 3D U-Net [J]. Frontiers in Public Health, 2023, 11: 1091850. [25] Allah AMG, Sarhan AM, Elshennawy NM. Edge U-Net: Brain tumor segmentation using MRI based on deep U-Net model with boundary information [J]. Expert Systems with Applications, 2023, 213: 118833. [26] 褚晶辉,黄凯隆,吕卫. 一种级联改进U-Net网络的脑肿瘤分割方法 [J]. 激光与光电子学进展, 2021, 58(8): 219-226. [27] Cinar N, Ozcan A, Kaya M. A hybrid DenseNet121-UNet model for brain tumor segmentation from MR Images [J]. Biomedical Signal Processing and Control, 2022, 76: 103647. [28] Raza R, Bajwa UI, Mehmood Y, et al. dResU-Net: 3D deep residual U-Net based brain tumor segmentation from multimodal MRI [J]. Biomedical Signal Processing and Control, 2023, 79: 103861. [29] Cao Yuan, Zhou Weifeng, Zang Min, et al. MBANet: a 3D convolutional neural network with multi-branch attention for brain tumor segmentation from MRI images [J]. Biomedical Signal Processing and Control, 2023, 80: 104296. [30] 单成响, 李锵, 关欣. 多视图卷积轻量级脑肿瘤分割算法 [J]. 激光与光电子学进展, 2023, 60(10): 202-209. [31] Liang Junjie, Yang Cihui, Zeng Mmengjie, et al. TransConver: transformer and convolution parallel network for developing automatic brain tumor segmentation in MRI images [J]. Quantitative Imaging in Medicine and Surgery, 2022, 12(4): 2397. [32] Jiang Yun, Zhang Yuan, Lin Xin, et al. SwinBTS: a method for 3D multimodal brain tumor segmentation using Swin transformer [J]. Brain sciences, 2022, 12(6): 797. [33] Ghazouani F, Vera P, Ruan S. Efficient brain tumor segmentation using Swin transformer and enhanced local self-attention [J]. International Journal of Computer Assisted Radiology and Surgery, 2024, 19(2): 273-281. [34] Liang Junjie, Yang Cihui, Zhong Jingting, et al. Btswin-unet: 3D U-shaped symmetrical swin transformer-based network for brain tumor segmentation with self-supervised pre-training [J]. Neural Processing Letters, 2023, 55(4): 3695-3713. [35] Alamri A, Abdel-Khalek S, Bahaddad AA, et al. Innovative deep learning and quantum entropy techniques for brain tumor MRI image edge detection and classification model [J]. Alexandria Engineering Journal, 2025, 122: 588-604. [36] Irmak E. Multi-classification of brain tumor MRI images using deep convolutional neural network with fully optimized framework [J]. Iranian Journal of Science and Technology, Transactions of Electrical Engineering, 2021, 45(3): 1015-1036. [37] Haq EU, Huang Jianjun, Li Kang, et al. An MRI-based deep learning approach for efficient classification of brain tumors [J]. Journal of Ambient Intelligence and Humanized Computing, 2023, 14(6): 6697-6718. [38] Agarwal M, Rani G, Kumar A, et al. Deep learning for enhanced brain tumor detection and classification [J]. Results in Engineering, 2024, 22: 102117. [39] 杜鹏,张有明,朱郑州,等. 迁移学习在低资源场景实体识别中的应用研究 [J]. 计算机科学与探索, 2023, 17(4): 912-921. [40] Sharma AK, Nandal A, Dhaka A, et al. Brain tumor classification using the modified ResNet50 model based on transfer learning [J]. Biomedical Signal Processing and Control, 2023, 86: 105299. [41] Mathivanan SK, Srinivasan S, Koti MS, et al. A secure hybrid deep learning framework for brain tumor detection and classification [J]. Journal of Big Data, 2025, 12: 72. [42] Demir F, Akbulut Y, Ta塂cı B, et al. Improving brain tumor classification performance with an effective approach based on new deep learning model named 3ACL from 3D MRI data [J]. Biomedical Signal Processing and Control, 2023, 81: 104424. [43] Modaresnia Y, Torghabeh FA, Hosseini SA. EfficientNetB0’s hybrid approach for brain tumor classification from MRI images using deep learning and bagging trees [C] //Computer and Knowledge Engineering. Mashhad: IEEE, 2023: 234-239. [44] Wolleb J, bieder F, Sandkühler R, et al. Diffusion models for medical anomaly detection [C] //Medical Image Computing and Computer-Assisted Intervention. Singapore: Springer Nature, 2022: 35-45. [45] Usman Akbar M, Larsson M, Blystad I, et al. Brain tumor segmentation using synthetic MR images-A comparison of GANs and diffusion models [J]. Scientific Data, 2024, 11(1): 259. [46] Wolleb J, Sandkühler R, Bieder F, et al. Diffusion models for implicit image segmentation ensembles [C] //Medical Imaging with Deep Learning. Zurich: PMLR, 2022: 1336-1348. [47] Rouzrokh P, Khosravi B, Faghani S, et al. Multitask brain tumor inpainting with diffusion models: A methodological report [EB/OL]. https://www.arxiv.org/abs/2210.12113,2022-10-21/2025-03-30. [48] Behrendt F, Bhattacharya D, Krüger J, et al. Patched diffusion models for unsupervised anomaly detection in brain MRI [C] //Medical Imaging with Deep Learning. Paris: PMLR, 2024: 1019-1032. [49] Gao Tao, Hu Weijie, Chen Mingzhi, et al. MSDMAT-BTS: multi-scale diffusion model and attention mechanism for brain tumor segmentation [J]. Biomedical Signal Processing and Control, 2025, 104: 107505. [50] Kebaili A, Lapuyade-Lahorgue J, Vera P, et al. Multi-modal MRI synthesis with conditional latent diffusion models for data augmentation in tumor segmentation [J]. Computerized Medical Imaging and Graphics, 2025, 123: 102532. [51] Wen Jie, Zhang Zhixia, Lan Yang, et al. A survey on federated learning: challenges and applications [J]. International Journal of Machine Learning and Cybernetics, 2023, 14(2): 513-535. [52] Tedeschini BC, Savazzi S, Stoklasa R, et al. Decentralized federated learning for healthcare networks: a case study on tumor segmentation [J]. IEEE Access, 2022, 10: 8693-8708. [53] Liu Hong, Wei Dong, Dai Qian, et al. Federated modality-specific encoders and partially personalized fusion decoder for multimodal brain tumor segmentation [J]. Medical Image Analysis, 2025, 106: 103759. [54] Onaizah AN, Xia Yuanqing, Hussain K. FL-SiCNN: an improved brain tumor diagnosis using siamese convolutional neural network in a peer-to-peer federated learning approach [J]. Alexandria Engineering Journal, 2025, 114: 1-11. [55] Islam M, Reza MT, Kaosar M, et al. Effectiveness of federated learning and CNN ensemble architectures for identifying brain tumors using MRI images [J]. Neural Processing Letters, 2023, 55(4): 3779-3809. [56] Lee WK, Hong JS, lin YH, et al. Federated learning: A cross-institutional feasibility study of deep learning based intracranial tumor delineation framework for stereotactic radiosurgery [J]. Journal of Magnetic Resonance Imaging, 2024, 59(6): 1967-1975. [57] Zhang C, Meng X, Liu Q, et al. FedBrain: a robust multi-site brain network analysis framework based on federated learning for brain disease diagnosis [J]. Neurocomputing, 2023, 559: 126791. [58] Mathew DE, Ebem DU, Ikegwu AC, et al. Recent emerging techniques in explainable artificial intelligence to enhance the interpretable and understanding of AI models for human[J]. Neural Processing Letters, 2025, 57: 16. [59] Khowaja SA, Dev K, Anwar SM, et al. SelfFed: Self-supervised federated learning for data heterogeneity and label scarcity in medical images[J]. Expert Systems with Applications, 2025, 261: 125493. [60] 王骁崴,邢树礼,毛国君.多尺度特征融合的轻量化Transformer医学图像分割研究[J].中国生物医学工程学报,2025,44(2):165-173.
