Brain Tumor Detection

Abstract

1. Introduction

This project aims to build 3 different models to detect tumors in brain MRIs (Magnetic Resonance Imaging) of different patients by using computer vision. Each model is trained and validated on one of the 3 possible planes generated by an MRI: axial, coronal and sagittal.

The outcomes of the models will show a colored box around a possible tumor or a structure that may resamble a tumor but it is not (in this case "Not tumor" label will be shown) and the confidence score for the detection.

The dataset used in this project has been edited and enlarged starting from this repository on Kaggle: Brain Tumor Object Detection Dataset. In total there are ~1.300 images and labels.

2. Objective

Brain tumor detection using computer vision

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

4. Technology Stack

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/giuseppebrb/BrainTumorDetection.git
cd BrainTumorDetection

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 giuseppebrb 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.