  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%.
