Abnormal growth of cells in the brain and its surrounding tissues is known as a brain tumor. There are two types, one is benign (non-cancerous) and another is malignant (cancerous) which may cause death. The radiologists' ability to diagnose malignancies is greatly aided by magnetic resonance imaging (MRI). Brain cancer diagnosis has been considerably expedited by the field of computer-assisted diagnostics, especially in machine learning and deep learning. In our study, we categorize three different kinds of brain tumors using four transfer learning techniques. Our models were tested on a benchmark dataset of $3064$ MRI pictures representing three different forms of brain cancer. Notably, ResNet-50 outperformed other models with a remarkable accuracy of $99.06\%$. We stress the significance of a balanced dataset for improving accuracy without the use of augmentation methods. Additionally, we experimentally demonstrate our method and compare with other classification algorithms on the CE-MRI dataset using evaluations like F1-score, AUC, precision and recall.

利用四种迁移学习技术对三种不同类型的脑肿瘤进行分类，在基准数据集中，ResNet-50模型以99.06%的显著准确率优于其他模型。我们强调，在不使用数据增强方法的情况下，通过保持数据集平衡以提高准确度的重要性，并通过F1分数、AUC、精确度和召回率等评估指标实验性地验证了我们的方法并与其他分类算法进行比较。

脑瘤分类的迁移学习的比较评估使用MRI