标题： 生成对抗网络（GAN）增强的脑肿瘤分类卷积神经网络中的比例敏感性 显示英文标题

标题： Proportional Sensitivity in Generative Adversarial Network (GAN)-Augmented Brain Tumor Classification Using Convolutional Neural Network

摘要： 生成对抗网络（GAN）在扩展有限的医学影像数据集方面显示出潜力。 本研究探讨了GAN生成与真实脑肿瘤MRI图像的不同比例如何影响卷积神经网络（CNN）在区分健康扫描与肿瘤扫描时的表现。 使用DCGAN生成合成图像，并以不同比例混合真实图像来训练定制的CNN。 然后，该CNN在独立的实际测试集上进行了评估。 我们的结果显示，即使模型主要基于合成数据进行训练，其在肿瘤分类中的敏感性和精确度仍然很高。 当只添加少量GAN数据时，例如900张真实图像和100张GAN图像，模型表现出色，测试准确率达到95.2%，并且精确度、召回率和F1分数均超过95%。 然而，随着GAN图像比例进一步增加，性能逐渐下降。 本研究表明，虽然GAN对于扩充有限的数据集非常有用，特别是当真实数据稀缺时，但过多的合成数据可能会引入影响模型泛化到实际案例的能力的伪影。 显示英文摘要

摘要： Generative Adversarial Networks (GAN) have shown potential in expanding limited medical imaging datasets. This study explores how different ratios of GAN-generated and real brain tumor MRI images impact the performance of a CNN in classifying healthy vs. tumorous scans. A DCGAN was used to create synthetic images which were mixed with real ones at various ratios to train a custom CNN. The CNN was then evaluated on a separate real-world test set. Our results indicate that the model maintains high sensitivity and precision in tumor classification, even when trained predominantly on synthetic data. When only a small portion of GAN data was added, such as 900 real images and 100 GAN images, the model achieved excellent performance, with test accuracy reaching 95.2%, and precision, recall, and F1-score all exceeding 95%. However, as the proportion of GAN images increased further, performance gradually declined. This study suggests that while GANs are useful for augmenting limited datasets especially when real data is scarce, too much synthetic data can introduce artifacts that affect the model's ability to generalize to real world cases.