推进高效脑肿瘤多类分类——视觉Mamba模型在迁移学习中的新见解
Advancing Efficient Brain Tumor Multi-Class Classification -- New Insights from the Vision Mamba Model in Transfer Learning
AI总结：
本研究首次将视觉Mamba模型应用于脑肿瘤多类分类，通过迁移学习微调，在独立测试集上实现100%准确率，证明其轻量高效，优于现有模型，并适用于其他医学影像分类。
摘要：
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，其检测和分类面临挑战。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。我们微调了多种主流迁移学习模型，并将其应用于脑肿瘤的多类分类。通过将这些模型与从零训练的模型进行比较，我们证明了迁移学习的显著优势，尤其是在标注数据往往有限的医学影像领域。值得注意的是，我们引入了视觉Mamba（Vim），一种新颖的网络架构，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达到了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，并揭示出与现有最先进模型相比，Vim模型轻量、高效且高度准确，为临床应用提供了新视角。此外，本研究提出的基于迁移学习和视觉Mamba模型的脑肿瘤分类框架，广泛适用于其他医学影像分类问题。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
