Brain Tumor Detection and Classification Using Deep Learning Models

Learn to develop an automated deep learning system for detecting and classifying brain tumors from MRI images in this 15-minute conference talk. Explore how three powerful deep learning models—VGG-16, VGG-19, and EfficientNet-B1—can be trained using transfer learning to classify brain tumors into four categories: Glioma, Meningioma, Pituitary tumor, and No tumor. Discover the preprocessing techniques including resizing, normalization, and data augmentation applied to a dataset of 7,023 MRI images to improve model performance and reduce overfitting. Compare the performance results where VGG-16 achieved approximately 88% accuracy, VGG-19 reached 90% accuracy, and EfficientNet-B1 delivered the highest accuracy of 94%. Understand how this automated system can support radiologists by accelerating diagnosis processes and increasing diagnostic reliability, while reducing the manual workload of scanning every MRI image. Gain insights into the practical clinical applications of deep learning in medical imaging and how high-accuracy models can provide patients with more reliable diagnostic results and greater confidence in their medical outcomes.