Brain Tumor Classification and Detection with VGG-16 using MRI Images

To improve the precision and treatment, classification and detection of brain tumors is important. In this study for classification and detection of brain tumors from MRI images, the VGG16[18] convolutional neural network (CNN) is used. In this study, the dataset consists of labeled MRI images[11] of patients having tumors and not having tumors. The proposed approach employs transfer learning with a pre-trained VGG16 network for feature extraction and fine-tuning for binary classification (tumor/no tumor). Scaling, normalization, and augmentation are examples of picture preprocessing methods used to increase dataset diversity. With an overall accuracy of 95.78% and an F1-score of 95.17% on the test set, the model proved to be successful in distinguishing between tumorous and non-tumorous areas. These promising results suggest that the VGG16-based approach can support improved clinical judgment by assisting in the timely and accurate diagnosis of brain tumors. Other CNN architectures[17] will be investigated in future research, and the dataset will be expanded for better generalization.