Brain Tumor Segmentation & Classification (CNN/U-Net)

Deep Learning models for Brain MRI image segmentation and multi-class tumor classification, focusing on Glioma, Meningioma, and Pituitary tumors.

Implemented and benchmarked multiple Convolutional Neural Network (CNN) architectures, including transfer learning with Xception for classification, a custom 2D U-Net for tumor segmentation, and an advanced 3D U-Net using NIfTI volumes for comprehensive volumetric analysis. The pipeline integrates custom loss functions (Dice/Jaccard), handles image preprocessing for diverse MRI modalities (FLAIR, T1, T1CE, T2), and uses TensorFlow/Keras for GPU-accelerated training. Project adheres to the Cookiecutter Data Science template for organization.