Brain tumors are increasingly prevalent, characterized by the uncontrolled
spread of aberrant tissues in the brain, with almost 700,000 new cases
diagnosed globally each year. Magnetic Resonance Imaging (MRI) is commonly used
for the diagnosis of brain tumors and accurate classification is a critical
clinical procedure. In this study, we propose an efficient solution for
classifying brain tumors from MRI images using custom transfer learning
networks. While several researchers have employed various pre-trained
architectures such as RESNET-50, ALEXNET, VGG-16, and VGG-19, these methods
often suffer from high computational complexity. To address this issue, we
present a custom and lightweight model using a Convolutional Neural
Network-based pre-trained architecture with reduced complexity. Specifically,
we employ the VGG-19 architecture with additional hidden layers, which reduces
the complexity of the base architecture but improves computational efficiency.
The objective is to achieve high classification accuracy using a novel
approach. Finally, the result demonstrates a classification accuracy of 96.42%.

使用自定义的转移学习网络，我们提出了一种通过 MRI 图像对脑肿瘤进行有效分类的解决方案，采用了 VGG-19 架构与额外隐含层的轻量模型，以降低计算复杂性并提高分类准确性，最终得到了 96.42% 的分类准确率。