用迁移学习提升脑瘤分类精度，EfficientNetV2表现最佳
Classification of Brain Tumors using Hybrid Deep Learning Models
- 采用迁移学习减少训练数据需求，对比EfficientNetV2、EfficientNet和ResNet50
- EfficientNetV2在三类脑瘤分类中准确率最高，达98.3%
- 适合医疗影像分析研究者，尤其关注轻量化模型部署
卷积神经网络（CNN）显著提升了医学图像解读能力，但传统CNN通常需要大量计算资源和训练数据。为克服这一限制，本研究采用迁移学习，在较少样本下实现优异分类性能。具体比较了EfficientNetV2、其前代EfficientNet及ResNet50在三类脑瘤（胶质瘤、脑膜瘤、垂体瘤）分类中的表现。结果表明，EfficientNetV2优于其他模型，但在复杂度增加的背景下，训练时间更长。
原文摘要 · Abstract (English)
The use of Convolutional Neural Networks (CNNs) has greatly improved the interpretation of medical images. However, conventional CNNs typically demand extensive computational resources and large training datasets. To address these limitations, this study applied transfer learning to achieve strong classification performance using fewer training samples. Specifically, the study compared EfficientNetV2 with its predecessor, EfficientNet, and with ResNet50 in classifying brain tumors into three types: glioma, meningioma, and pituitary tumors. Results showed that EfficientNetV2 delivered superior performance compared to the other models. However, this improvement came at the cost of increased training time, likely due to the model's greater complexity.
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