Comparative Deep Learning Framework for Brain Tumor Detection: Integrating VGG16 Classification and U-Net Segmentation with Performance Evaluation

Brain tumors are serious and potentially life-threatening, making early and accurate diagnosis essential for improving survival chances. MRI (Magnetic Resonance Imaging) is a key tool in detecting these tumors without invasive procedures, but analyzing MRI scans manually takes time and can lead to mistakes. In this study, we explore how deep learning—specifically Convolutional Neural Networks (CNNs)—can help automate and improve brain tumor detection using MRI images. We compare the performance of four CNN-based models: EfficientNetB3, VGG16 with fine-tuned transfer learning, ResNet-50, and a custom-built CNN. Our goal is to find out which model performs best in terms of accuracy, precision, recall, and F1-score. After extensive testing, the fine-tuned VGG16 model came out on top as the most accurate and dependable. To build a complete diagnostic system, we combined the VGG16 model with U-Net architecture for tumor segmentation. This approach not only classifies the type of brain tumor—glioma, meningioma, pituitary tumor, or no tumor—but also pinpoints its location within the MRI scan. The segmentation achieved strong results, with an IoU score of 0.87 and a Dice score of 0.91.