Abstract
Enhanced MRI Tumor Classification Web App is an open-source AI & Machine Learning project. Reliable MRI brain tumor detection for various types including pituitary, meningioma, glioma. Focused on high accuracy in diverse image qualities. Features a Streamlit web app for practical deployment. In this project, we have developed a machine learning model focused on accurately classifying MRI brain images into categories such as normal, pituitary tumor, meningioma tumor, and glioma tumor. Recognizing the critical need for precise diagnoses, especially in regions with limited access to high-quality medical imaging, this model is fine-tuned to perform exceptionally well on both high-quality and low-quality images. It is built using Jupyter Notebook. 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
In this project, we have developed a machine learning model focused on accurately classifying MRI brain images into categories such as normal, pituitary tumor, meningioma tumor, and glioma tumor. Recognizing the critical need for precise diagnoses, especially in regions with limited access to high-quality medical imaging, this model is fine-tuned to perform exceptionally well on both high-quality and low-quality images. This approach ensures robust generalization and broad utility in various healthcare environments. Beyond the model development, a significant part of this project involves the implementation of a Web Application. Utilizing Streamlit, we have deployed the trained model into an interactive web app, enabling users to upload MRI images and receive instant classification results. To ensure easy and reliable deployment, the entire application has been containerized using Docker. This approach simplifies the process of setting up the app in different environments, making it accessible and easy to use, regardless of the underlying platform.
2. Objective
Reliable MRI brain tumor detection for various types including pituitary, meningioma, glioma. Focused on high accuracy in diverse image qualities. Features a Streamlit web app for practical deployment.
This project demonstrates how Jupyter Notebook 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/FarzadNekouee/Enhanced_MRI_Tumor_Classification_Web_App.git
cd Enhanced_MRI_Tumor_Classification_Web_AppFull 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.
- What dataset does the project use and how was it pre-processed?
- Which algorithm / model architecture is used and why was it chosen over alternatives?
- How are training and testing data split, and how is overfitting avoided?
- Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
- How would you deploy this model for real users?
9. Source Code & License
This project is developed by FarzadNekouee 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.
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