Early and accurate diagnosis of brain tumors is crucial for improving patient survival rates. However, the detection and classification of brain tumors are challenging due to their diverse types and complex morphological characteristics. This study investigates the application of pre-trained models for brain tumor classification, with a particular focus on deploying the Mamba model. We fine-tuned several mainstream transfer learning models and applied them to the multi-class classification of brain tumors. By comparing these models to those trained from scratch, we demonstrated the significant advantages of transfer learning, especially in the medical imaging field, where annotated data is often limited. Notably, we introduced the Vision Mamba (Vim), a novel network architecture, and applied it for the first time in brain tumor classification, achieving exceptional classification accuracy. Experimental results indicate that the Vim model achieved 100% classification accuracy on an independent test set, emphasizing its potential for tumor classification tasks. These findings underscore the effectiveness of transfer learning in brain tumor classification and reveal that, compared to existing state-of-the-art models, the Vim model is lightweight, efficient, and highly accurate, offering a new perspective for clinical applications. Furthermore, the framework proposed in this study for brain tumor classification, based on transfer learning and the Vision Mamba model, is broadly applicable to other medical imaging classification problems.

翻译：脑肿瘤的早期准确诊断对于提高患者生存率至关重要。然而，由于脑肿瘤类型多样且形态特征复杂，其检测与分类具有挑战性。本研究探讨了预训练模型在脑肿瘤分类中的应用，特别关注Mamba模型的部署。我们对多个主流迁移学习模型进行了微调，并将其应用于脑肿瘤的多类别分类任务。通过将这些模型与从头训练的模型进行比较，我们证明了迁移学习在医学影像领域（尤其是标注数据通常有限的情况下）具有显著优势。值得注意的是，我们引入了新颖的网络架构Vision Mamba（Vim），并首次将其应用于脑肿瘤分类，取得了卓越的分类准确率。实验结果表明，Vim模型在独立测试集上实现了100%的分类准确率，凸显了其在肿瘤分类任务中的潜力。这些发现强调了迁移学习在脑肿瘤分类中的有效性，并揭示出与现有先进模型相比，Vim模型具有轻量化、高效且高精度的特点，为临床应用提供了新的视角。此外，本研究提出的基于迁移学习和Vision Mamba模型的脑肿瘤分类框架，可广泛适用于其他医学影像分类问题。