Brain Tumor Detection and Classification via VGG16-Based Deep Learning on MRI Imaging

Brain tumor detection and diagnosis are critical medical imaging procedures that directly impact patient care and outcomes. This article presents a deep learning solution with the VGG16 convolutional neural network (CNN) to detect brain tumors from magnetic resonance imaging (MRI) scans automatically. The method incorporates rigorous image preprocessing through normalization and augmentation to enhance model generalizability. Transfer learning is applied through the fine-tuning of the VGG16 model with pre-trained weights for enhancing feature extraction. The model was trained on a dataset of 7,000 MRI images, which were classified as tumor and non-tumor. The experimental results indicate that the proposed model has a training accuracy of 100% and a validation accuracy of 94%, in addition to a steadily decreasing loss, which shows a high capability for generalization. The classification performance is also verified using precision, recall, F1-score, and a confusion matrix for checking its robustness. The approach points to the potential of deep learning in medical image analysis as a reliable and automatic framework for brain tumor detection and early diagnosis.