Brain Tumor Detection

Brain tumor detection and prediction using keras vgg-16

AI & Machine LearningJupyter NotebookApache-2.0

Abstract

Brain Tumor Detection is an open-source AI & Machine Learning project. Brain tumor detection and prediction using keras vgg-16. It is built using Jupyter Notebook, Deep Learning, FastAPI, Flask, Keras. 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

Brain tumor detection and prediction using keras vgg-16

Input: The model takes in a brain scan as input. The input is typically a 3D array of pixel values representing the image.

Convolutional Layers: The input is passed through a series of convolutional layers, each of which extracts increasingly complex features from the input image. The convolutional layers use filters to scan the image and identify patterns and structures.

2. Objective

Brain tumor detection and prediction using keras vgg-16

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

4. Technology Stack

Jupyter NotebookDeep LearningFastAPIFlaskKerasMachine LearningReact

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/rishavchanda/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 rishavchanda 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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