Multi-EfficientNet ensemble-based brain tumor MRI images classification

Early screening for tumor symptoms through magnetic resonance imaging (MRI) is crucial for the timely diagnosis and treatment of brain tumor patients. To address the challenge of poor prediction accuracy in single deep learning models, a ensemble learning based brain tumor image classification approach with multiple EfficientNet models is proposed in this paper. Multiple EfficientNet models are integrated, and their feature maps are fused through a spatial channel attention module. Experimental results demonstrate a significant improvement in accuracy compared to other single-model prediction methods. The F1-Score for the four classes, Glioma, Meningioma, Pituitary, and Normal, reached 0.95, 0.95, 0.98, and 0.96, respectively. The proposed method provides robust support for the early diagnosis and treatment of brain tumor patients and holds promising prospects for wide clinical applications.