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%.

翻译：脑肿瘤发病率日益增高，其特征为脑部异常组织的不可控扩散，全球每年新确诊病例近70万例。磁共振成像（MRI）常被用于脑肿瘤诊断，而精准分类是一项关键的临床流程。本研究提出了一种利用自定义迁移学习网络对MRI图像中的脑肿瘤进行高效分类的解决方案。尽管已有研究采用RESNET-50、ALEXNET、VGG-16和VGG-19等多种预训练架构，但这些方法往往存在计算复杂度高的问题。为解决这一难题，我们提出了一种基于卷积神经网络预训练架构的低复杂度自定义轻量级模型。具体而言，采用VGG-19架构并增设隐藏层，在降低基础架构复杂度的同时提升计算效率。目标是通过创新方法实现高分类精度。最终结果表明，该方法的分类准确率达到96.42%。