This study presents a brain tumor detection and classification system using transfer learning models. The research evaluates three foundational computer vision models—AlexNet, VGG16, and ResNet-50—on a dataset of 3264 MRI images of brain tumors. The VGG16 and ResNet-50 models showed strong performance, leading to the development of a hybrid VGG16–ResNet-50 model. This hybrid model achieved an accuracy of 99.98%, with high sensitivity and specificity of 99.98% and an F1 score of 99.98%. The study also compares the performance of these models with other existing methods, demonstrating the effectiveness of the hybrid model in brain tumor classification. The study highlights the importance of deep learning in medical imaging for accurate and efficient brain tumor detection. The hybrid model outperforms individual models, showing superior performance in classifying brain tumors. The research contributes to the field of medical imaging by providing a reliable and accurate method for brain tumor detection and classification. The study also discusses the challenges in brain tumor detection, including the need for accurate diagnosis and the limitations of traditional methods. The results indicate that the proposed hybrid model is a promising solution for early and accurate brain tumor detection, which can aid in timely treatment and improve patient outcomes. The study emphasizes the potential of transfer learning in medical imaging and the need for further research to refine and understand these models.This study presents a brain tumor detection and classification system using transfer learning models. The research evaluates three foundational computer vision models—AlexNet, VGG16, and ResNet-50—on a dataset of 3264 MRI images of brain tumors. The VGG16 and ResNet-50 models showed strong performance, leading to the development of a hybrid VGG16–ResNet-50 model. This hybrid model achieved an accuracy of 99.98%, with high sensitivity and specificity of 99.98% and an F1 score of 99.98%. The study also compares the performance of these models with other existing methods, demonstrating the effectiveness of the hybrid model in brain tumor classification. The study highlights the importance of deep learning in medical imaging for accurate and efficient brain tumor detection. The hybrid model outperforms individual models, showing superior performance in classifying brain tumors. The research contributes to the field of medical imaging by providing a reliable and accurate method for brain tumor detection and classification. The study also discusses the challenges in brain tumor detection, including the need for accurate diagnosis and the limitations of traditional methods. The results indicate that the proposed hybrid model is a promising solution for early and accurate brain tumor detection, which can aid in timely treatment and improve patient outcomes. The study emphasizes the potential of transfer learning in medical imaging and the need for further research to refine and understand these models.