Brain tumor classification from MRI images using a multi-scale channel attention CNN integrated with SVM

Accurate classification of brain tumors from Magnetic Resonance Imaging (MRI) images remains a significant technical challenge in medical image analysis. Recent advancements have primarily focused on developing automated image classification methods. However, traditional convolutional neural networks (CNNs) have limited feature extraction capabilities, leading to suboptimal recognition performance. To address this issue, this paper proposes a novel image classification framework named multi-scale channel attention CNN integrated with support vector machine (MCACNN-SVM). In the proposed MCACNN-SVM, hierarchical spatial features are extracted with multi-scale convolutional kernels, and then further adaptively enhanced by adopting the channel attention mechanism. Finally, an SVM classifier optimized by grid search algorithm is employed to optimize the decision boundaries and enhance classification accuracy. Furthermore, the cosine annealing with warm restarts strategy is adopted to accelerate convergence and improve generalization. Extensive experiments on the brain tumor MRI dataset demonstrate that the proposed framework achieves competitive performance in accuracy, precision, recall, and F1-score, exhibiting strong robustness and generalization ability for practical applications.