Brain Tumor Detection

Brain Tumor Detection Using Convolutional Neural Networks.

AI & Machine LearningJupyter NotebookApache-2.0

Abstract

Brain Tumor Detection is an open-source AI & Machine Learning project. Brain Tumor Detection Using Convolutional Neural Networks. Building a detection model using a convolutional neural network in Tensorflow & Keras. Used a brain MRI images data founded on Kaggle. It is built using Jupyter Notebook, Deep Learning. The complete source code is publicly available on GitHub under the Apache License 2.0, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

Building a detection model using a convolutional neural network in Tensorflow & Keras. Used a brain MRI images data founded on Kaggle. You can find it here.

The dataset contains 2 folders: yes and no which contains 253 Brain MRI Images. The folder yes contains 155 Brain MRI Images that are tumorous and the folder no contains 98 Brain MRI Images that are non-tumorous.

Since this is a small dataset, There wasn't enough examples to train the neural network. Also, data augmentation was useful in taclking the data imbalance issue in the data.

2. Objective

Brain Tumor Detection Using Convolutional Neural Networks.

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

4. Technology Stack

Jupyter NotebookDeep Learning

5. System Requirements

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

  • Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
  • pip for dependencies
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/MohamedAliHabib/Brain-Tumor-Detection.git
cd Brain-Tumor-Detection

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 MohamedAliHabib and published on GitHub under the Apache License 2.0. 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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