Computer Science ›› 2024, Vol. 51 ›› Issue (6A): 230600043-7.doi: 10.11896/jsjkx.230600043

• Image Processing & Multimedia Technolog • Previous Articles     Next Articles

Intelligent Diagnosis of Brain Tumor with MRI Based on Ensemble Learning

LI Xinrui1, ZHANG Yanfang2, KANG Xiaodong1, LI Bo3, HAN Junling1,4   

  1. 1 School of Medical Imaging,Tianjin Medical University,Tianjin 300202,China
    2 Chongqing University Qianjiang Hospital,Chongqing 409000,China
    3 Tianjin Third Central Hospital,Tianjin 300170,China
    4 Tianjin First Central Hospital,Tianjin 300192,China
  • Published:2024-06-06
  • About author:LI Xinrui,born in 2002,undergraduate.Her main research interest is medical image processing.
    ZHANG Yanfang,born in 1975,bachelor.Her main research interests include imaging research of cardiovascular and cerebrovascular diseases.
  • Supported by:
    Beijing-Tianjin-Hebei Collaborative Innovation Project(17YEXTZC00020).

Abstract: Brain tumors are high-risk diseases caused by cancerous changes in the internal tissues of the brain,and timely diagnosis of brain tumors is crucial for their treatment and prognosis.At present,different network models have different classification effects,and a single network model is difficult to achieve outstanding performance on multiple evaluation indicators.This paper proposes a Treer-Net model with powerful classification function based on ensemble learning,which is based on TransFG,ResNet50,EfficientNet B4,EfficientNet B7 and ResNeXt101,and is obtained through the weighted average combination strategy of ensemble learning.This paper trains it to complete the classification tasks on the publicly available datasets of brain tumor MRI binary,tertiary and quaternary classifications.Experimental data and results show that the accuracy,precision recall and AUC of the Treer-Net model in the three classification datasets of brain tumors are up to 99.15%,99.16%,99.15% and 99.87% respectively.Through comparative analysis,it fully verifies that the ensemble learning method in this paper has the advantages of accuracy and speed,and is more suitable for clinical auxiliary diagnosis of brain tumors.

Key words: Brain tumor, Ensemble learning, Image classification, Magnetic resonance imaging

CLC Number: 

  • TP391
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