<![CDATA[Brain tumors are one of the most critical and life-threatening health conditions requiring rapid and accurate diagnostic support. Early detection plays a crucial role in determining appropriate medical interventions and improving patient survival rates. With advances in artificial intelligence, particularly computer vision, medical image transmission has emerged as a promising field to address the challenges of manual diagnosis, which is often time-consuming and prone to human error. Magnetic resonance imaging (MRI) is widely used in brain imaging due to its ability to provide detailed structural information, making it an ideal modality for tumor detection and classification. This study employs a Convolutional Neural Network (CNN)-based approach that integrates two deep learning architectures: VGG16 and ResNet50V2, using batch normalization to improve feature extraction and reduce overfitting. Evaluation experiments were conducted on an MRI dataset of 1,311 brain tumor MRI images classified into pituitary, notoma, meningioma, and glioma classes. The aim of this study was to develop a fast, accurate, and efficient method for detecting brain tumors. The results show that the proposed hybrid architecture achieves 98% accuracy, outperforming each pretrained model when applied separately. This study demonstrates that combining multiple CNN architectures with batch normalization can significantly improve the precision and accuracy of brain tumor detection. This approach has the potential to become a valuable diagnostic tool for radiologists, enabling faster and more accurate clinical decision-making. Furthermore, the application of such deep learning models in medical practice could contribute to reducing diagnostic errors and improving patient care in the long term.]]>
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