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  4. Vol 7, Issue 8, 2023

Review of deep learning-driven MRI brain tumor detection and segmentation methods

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DOI: 10.23977/acss.2023.070803 | Downloads: 36 | Views: 502

Author(s)

Rong Zhang 1, Hongliang Luo 1, Weijie Chen 1, Yongqiang Bai 1

Affiliation(s)

1 Zhejiang Wanli University, Ningbo, Zhejiang, 315000, China

Corresponding Author

Rong Zhang

ABSTRACT

The application of deep learning in the field of medical imaging has become increasingly widespread, greatly promoting the advancement and development of Magnetic Resonance Imaging (MRI) brain tumor detection and segmentation techniques. Therefore, a comprehensive review of deep learning-based methods for MRI brain tumor detection and segmentation was conducted. This review introduces the basic concepts of brain tumors and MRI brain tumor detection and segmentation, discusses the specific applications and typical methods of deep learning in MRI brain tumor detection and segmentation, and analyzes and compares the performance and advantages and disadvantages of different methods. Additionally, representative brain tu-mor segmentation dataset (BraTS) and its evaluation metrics are introduced, upon which the performance of various deep learning-based brain tumor segmentation methods on the BraTS 2019-2022 dataset is compared. Lastly, the challenges and future development trends in deep learning-based MRI brain tumor detection and segmentation methods are summarized and anticipated.

KEYWORDS

Deep Learning, Brain Tumor, Detection and Segmentation, Magnetic Resonance Imaging

CITE THIS PAPER

Rong Zhang, Hongliang Luo, Weijie Chen, Yongqiang Bai, Review of deep learning-driven MRI brain tumor detection and segmentation methods. Advances in Computer, Signals and Systems (2023) Vol. 7: 17-28. DOI: http://dx.doi.org/10.23977/acss.2023.070803.

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