The study evaluates the effectiveness of transfer learning models in brain tumor detection and classification using MRI images. The authors compared three foundational models—AlexNet, VGG16, and ResNet-50—and found that VGG16 and ResNet-50 performed well. These models were then integrated into a hybrid VGG16-ResNet-50 model, which achieved remarkable accuracy, sensitivity, specificity, and F1 score of 99.98%. The study highlights the potential of deep learning methodologies in medical imaging, particularly in brain tumor classification, and suggests that the proposed framework can facilitate timely and accurate identification of cerebral neoplasms. The research aims to reduce global fatality rates and improve clinical diagnosis and therapeutic decision-making for brain tumor patients. Future work will focus on refining these models and understanding their inner workings.The study evaluates the effectiveness of transfer learning models in brain tumor detection and classification using MRI images. The authors compared three foundational models—AlexNet, VGG16, and ResNet-50—and found that VGG16 and ResNet-50 performed well. These models were then integrated into a hybrid VGG16-ResNet-50 model, which achieved remarkable accuracy, sensitivity, specificity, and F1 score of 99.98%. The study highlights the potential of deep learning methodologies in medical imaging, particularly in brain tumor classification, and suggests that the proposed framework can facilitate timely and accurate identification of cerebral neoplasms. The research aims to reduce global fatality rates and improve clinical diagnosis and therapeutic decision-making for brain tumor patients. Future work will focus on refining these models and understanding their inner workings.