Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月27日

查看全文
  • Longzhang Ke,
  • Guozhen Hu,
  • Min Zhao,
  • Zhi Liu,
  • Zean Lv,
  • Yuqing Yang

摘要

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.