Abstract
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.
Abstract (translated)
卷积神经网络（CNN）的使用极大地提高了医学影像的解读能力。然而，传统的CNN通常需要大量的计算资源和大规模的训练数据集。为了克服这些限制，本研究应用了迁移学习，在较少的训练样本下实现了较强的分类性能。具体来说，该研究将EfficientNetV2与其前身EfficientNet以及ResNet50进行了比较，用于对大脑肿瘤进行三类（胶质瘤、脑膜瘤和垂体瘤）的分类。结果表明，EfficientNetV2相比其他模型表现出更优的性能。然而，这种改进是以训练时间增加为代价的，这可能是由于该模型复杂度更高所致。
URL
https://arxiv.org/abs/2508.01350
