DevConf.IN 2026

Brain tumors are life-threatening and require early and accurate diagnosis. MRI scans are widely used for detection. Brain tumors can look different from one another in MRI scans, so identifying them correctly is very important. To solve this problem, our project develops an automated deep learning-based system that detects and classifies brain tumors directly from MRI images into four types that are Glioma, Meningioma, Pituitary tumor, and No tumor
We trained and evaluated three powerful deep learning models using transfer learning — VGG-16, VGG-19, and EfficientNet-B1 — on a dataset of 7023 MRI images. The system applies resizing, normalization, and data augmentation to improve learning and reduce overfitting. During evaluation, VGG-16 achieved ~88% accuracy, VGG-19 achieved ~90% accuracy, and EfficientNet-B1 achieved the highest accuracy of ~94%, making it the most efficient model for multi-class tumor classification.
This work shows that deep learning can greatly support radiologists by speeding up diagnosis and increasing reliability. The results prove that EfficientNet-B1 is a strong option for real-world clinical applications because it provides high accuracy, low loss, and fast training with fewer parameters.
Key Takeaways:
• It reduces the need to manually check every scan and supports doctors in making decisions with more confidence.
• The project shows how deep learning can be used to solve a real medical problem using MRI image data.
• High accuracy means lower chances of misclassification, giving patients peace of mind about their results.