Brain Tumor Clf TDA

This repository is the official code for the paper "Enhanced MRI Brain Tumor Detection and Classification via Topological Data Analysis and Low-Rank Tensor Decomposition" by Serena Grazia De Benedictis, Grazia Gargano and Gaetano Settembre.

AI & Machine LearningPythonGPL-3.0

Abstract

Brain Tumor Clf TDA is an open-source AI & Machine Learning project. This repository is the official code for the paper "Enhanced MRI Brain Tumor Detection and Classification via Topological Data Analysis and Low-Rank Tensor Decomposition" by Serena Grazia De Benedictis, Grazia Gargano and Gaetano Settembre. It is built using Python, Machine Learning. The complete source code is publicly available on GitHub under the GNU General Public License v3.0, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

This repository is the official code for the paper "Enhanced MRI Brain Tumor Detection and Classification via Topological Data Analysis and Low-Rank Tensor Decomposition" by Serena Grazia De Benedictis, Grazia Gargano and Gaetano Settembre.\ [](https://juleskreuer.eu/citation-badge/)

2. Objective

This repository is the official code for the paper "Enhanced MRI Brain Tumor Detection and Classification via Topological Data Analysis and Low-Rank Tensor Decomposition" by Serena Grazia De Benedictis, Grazia Gargano and Gaetano Settembre.

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

4. Technology Stack

PythonMachine Learning
  • Tensorly
  • Matplotlib
  • Scikit-learn

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/gaetanosettembre/BrainTumor_clf_TDA.git
cd BrainTumor_clf_TDA

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 gaetanosettembre and published on GitHub under the GNU General Public License v3.0. 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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