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MRI brain tumor classification based on multi-scale residual network
HUANG Min  XIONG Zhengyun  ZHU Junlin 

Cite this article as: HUANG M , XIONG Z Y, ZHU J L. MRI brain tumor classification based on multi-scale residual network[J]. Chin J Magn Reson Imaging, 2023, 14(1): 124-129. DOI:10.12015/issn.1674-8034.2023.01.022.

[Abstract] Objective Research and build artificial intelligence deep learning network to achieve high accuracy MRI brain tumor four classification on two public brain MR image datasets.Materials and Methods We propose a multi-scale residual network for MRI brain tumor classification model to achieve the task of four brain tumor classification. The model consists of four modules: multi-scale input, improved residuals, down-sampling, and dual-channel pooling. Brain MR images from normal subjects in Kaggle and tumor patients in Figshare are combined to train and evaluate the performance of the proposed model.Results The model is tested on 352 MR images. When only multi-scale input module is used, the average classification accuracy is 96.59%. After adding the subsampling module, the accuracy reaches 98.58%. Compare max-pooling, mean-pooling and dual-channel pooling, the accuracy is 96.02%, 97.16% and 98.58%, respectively. The multi-scale residual network has a good classification effect on brain tumors, and the classification accuracy of glioma, meningioma, pituitary tumor and normal images is 99.14%, 99.14%, 99.42% and 99.42%, respectively.Conclusions MRI is a typical medical imaging method for the examination of brain tumors, but the accurate classification of brain tumors manually by radiologists is extremely subjective and uncertain. The proposed multi-scale residual network can provide an effective method for automatic classification of brain tumors, and it can improve the accuracy of MRI brain tumor classification. It solves the problem of gradient vanishing well and improves the generalization ability of the model.
[Keywords] artificial intelligence;deep learning;magnetic resonance imaging;brain tumor classification;multi-scale residual network;down-sampling;dual-channel pooling;convolutional neural network

HUANG Min1, 2*   XIONG Zhengyun1   ZHU Junlin1  

1 School of Biomedical Engineering, South-Central Minzu University, Wuhan 430074, China

2 Hubei Key Laboratory of Medical Information Analysis and Tumor Diagnosis & Treatment, Wuhan 430074, China

Corresponding author: Huang M, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS Natural Science Foundation of Hubei Province (No. 2020CFB837).
Received  2022-08-12
Accepted  2022-11-14
DOI: 10.12015/issn.1674-8034.2023.01.022
Cite this article as: HUANG M , XIONG Z Y, ZHU J L. MRI brain tumor classification based on multi-scale residual network[J]. Chin J Magn Reson Imaging, 2023, 14(1): 124-129. DOI:10.12015/issn.1674-8034.2023.01.022.

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