BRATS Brain Tumor Detection And Segmentation Twopathway CNN

PyTorch implementation of Two Pathway CNN and Cascaded architectures for automatic brain tumor segmentation on BraTS 2020 MRI dataset. Implements TwoPathCNN, InputCascadeCNN, LocalCascadeCNN, and MFCascadeCNN with two-phase training and fully convolutional inference.

AI & Machine LearningPythonMIT

Abstract

BRATS Brain Tumor Detection And Segmentation Twopathway CNN is an open-source AI & Machine Learning project. PyTorch implementation of Two Pathway CNN and Cascaded architectures for automatic brain tumor segmentation on BraTS 2020 MRI dataset. Implements TwoPathCNN, InputCascadeCNN, LocalCascadeCNN, and MFCascadeCNN with two-phase training and fully convolutional inference. Adapted for the BraTS 2020 dataset (494 subjects, 4 MRI modalities). It is built using Python, Deep Learning, PyTorch. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

Adapted for the BraTS 2020 dataset (494 subjects, 4 MRI modalities).

All convolutions use valid mode (no padding). Max pooling uses stride 1 to preserve per-pixel accuracy. Maxout activation with K=2 replaces traditional nonlinearities.

2. Objective

PyTorch implementation of Two Pathway CNN and Cascaded architectures for automatic brain tumor segmentation on BraTS 2020 MRI dataset. Implements TwoPathCNN, InputCascadeCNN, LocalCascadeCNN, and MFCascadeCNN with two-phase training and fully convolutional inference.

This project demonstrates how Python, Deep Learning, PyTorch can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

PythonDeep LearningPyTorch

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Python 3.8 or later
  • pip / virtualenv for dependencies
  • VS Code, PyCharm or Jupyter Notebook
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/kbhujbal/BRATS_brain_tumor_detection_and_segmentation_twopathwayCNN.git
cd BRATS_brain_tumor_detection_and_segmentation_twopathwayCNN
# Clone and install
git clone <repo-url>
cd BRATS_brain_tumor_detection_and_segmentation_twopathwayCNN
pip install -r requirements.txt

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Deploy the model as a web app with Streamlit, Flask or FastAPI
  • Compare against an additional model and report the metric difference
  • Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What dataset does the project use and how was it pre-processed?
  2. Which algorithm / model architecture is used and why was it chosen over alternatives?
  3. How are training and testing data split, and how is overfitting avoided?
  4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
  5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by kbhujbal and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.

View Source on GitHubDownload ZIP

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