基于深度学习的脑肿瘤MRI图像诊断研究进展

doi:10.3969/j.issn.0258-8021.2026.01.008

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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.

Key words： deep learning brain tumor magnetic resonance imaging convolutional neural network diffusion model federated learning

Received: 14 May 2025

PACS:

R318

Corresponding Authors: ^*E-mail:whjade@126.com

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	Jiang Liang
	Ma Xingmin
	Wang Hua

Cite this article:

Jiang Liang,Ma Xingmin,Wang Hua. Research Progress of Brain Tumor MRI Image Diagnosis Based on Deep Learning[J]. Chinese Journal of Biomedical Engineering, 2026, 45(1): 79-86.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2026.01.008 OR http://cjbme.csbme.org/EN/Y2026/V45/I1/79

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