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Brain MRI Tumor Segmentation

This project implements a deep learning pipeline for detecting and segmenting brain tumors in MRI images.

Overview

The system uses a two-stage approach:

  1. A classification model to detect the presence of tumors
  2. A segmentation model to localize and outline tumors when detected

Key components:

  • Data preprocessing and visualization
  • ResNet50-based classification model
  • ResUNet segmentation model
  • Custom loss functions (Focal Tversky loss)
  • Evaluation metrics and result visualization

Dataset

The project uses the LGG MRI Segmentation dataset, containing brain MRI images and corresponding tumor mask annotations.

Models

Classification Model

  • Based on ResNet50 architecture
  • Detects presence/absence of tumors

Segmentation Model

  • Custom ResUNet architecture
  • Generates pixel-wise tumor masks

Usage

The main notebook BrainMRIseg.ipynb contains the full pipeline:

  1. Data loading and preprocessing
  2. Model training (classification and segmentation)
  3. Evaluation and visualization of results

Results

  • Classification accuracy: 93.75%
  • Segmentation Tversky score: 85.10%

Combining classification and segmentation can potentially reduce false positives and provide more detailed information about tumor presence and location.

Sample segmentation results are visualized in the notebook.

Requirements

  • TensorFlow 2.x
  • Keras
  • OpenCV
  • scikit-image
  • pandas
  • matplotlib

Future Work

  • Experiment with other architectures
  • Implement additional data augmentation
  • Explore multi-class segmentation for different tumor types