Precision Brain Tumor Detection using Machine Learning

Early Detection of brain tumors is essential not only for effective diagnosis but also for improving patient outcomes. This study introduces an automated brain tumor classification system using Deep Learning methods, applied to MRI scans. The framework leverages MobileNetV2 architecture through transfer learning, letting the model to quickly acquire meaningful features from MRI images though maintaining a lightweight structure appropriate for real-world deployment. The proposed system includes a thorough preprocessing pipeline, involving normalization, data augmentation, and generalization techniques to minimize overfitting and improve the model’s ability to classify unseen data. It is estimated using standard performance metrics—accuracy, precision, recall, and F1 score—and achieves a test accuracy of 90.32%, indicating strong potential for clinical use. The effectiveness of transfer learning is also highlighted through a comparison with a baseline convolutional neural network (CNN), demonstrating notable gains in classification accuracy. This approach not only has the potential to support radiologists by improving diagnostic accuracy and speed but also offers a step toward integrating reliable, automated tools into the clinical workflow.