Intelligent Diagnosis of Brain Tumor with MRI Based on Ensemble Learning
Brain tumors are high-risk diseases caused by cancerous changes in the internal tissues of the brain,and timely diagno-sis 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,Res-Net50,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％respec-tively.Through comparative analysis,it fully verifies that the ensemble learning method in this paper has the advantages of accu-racy and speed,and is more suitable for clinical auxiliary diagnosis of brain tumors.
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