Abstract
Early and accurate brain tumor classification is vital for clinical diagnosis and treatment. Although Convolutional Neural Networks (CNNs) are widely used in medical image analysis, they often struggle to focus on critical information adequately and have limited feature extraction capabilities. To address these challenges, this study proposes a novel Residual Network based on Multi-dimensional Attention and Pinwheel Convolution (Res-MAPNet) for Magnetic Resonance Imaging (MRI) based brain tumor classification. Res-MAPNet is developed on two key modules: the Coordinated Local Importance Enhancement Attention (CLIA) module and the Pinwheel-Shaped Attention Convolution (PSAConv) module. CLIA combines channel attention, spatial attention, and direction-aware positional encoding to focus on lesion areas. PSAConv enhances spatial feature perception through asymmetric padding and grouped convolution, expanding the receptive field for better feature extraction. The proposed model classifies two publicly brain tumor datasets into glioma, meningioma, pituitary tumor, and no tumor. The experimental results show that the proposed model achieves 99.51% accuracy in the three-classification task and 98.01% accuracy in the four-classification task, better than the existing mainstream models. Ablation studies validate the effectiveness of CLIA and PSAConv, which are 4.41% and 4.45% higher than the ConvNeXt baseline, respectively. This study provides an efficient and robust solution for brain tumor computer-aided diagnosis systems with potential for clinical applications.