Brain MRI Segmentation

A deep learning project for segmenting brain tumors in MRI images using U-Net and YOLO architectures.

Overview

This project implements brain tumor segmentation using state-of-the-art deep learning models. It includes a U-Net implementation for semantic segmentation and YOLO configuration for object detection-based approaches.

Features

• U-Net Architecture: Custom implementation of U-Net with encoder-decoder structure and skip connections
• Data Processing: Jupyter notebook for data mapping and preprocessing
• YOLO Integration: Configuration for YOLO-based tumor detection
• PyTorch Implementation: Built with PyTorch for flexibility and performance

Requirements

Install the required dependencies:

pip install -r requirements.txt

Main dependencies include:

• PyTorch
• torchvision
• OpenCV
• albumentations
• segmentation_models_pytorch
• ultralytics (YOLO)
• matplotlib
• pandas