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MACHINE-LEARNING Project
Brain Tumor Detection System Using Machine Learning Source Code
Get runnable Brain Tumor Detection System Using Machine Learning Source Code with database files, project setup instructions, live demo options and installation support. This project resource helps students understand the implementation, modules, workflow and technical architecture of a complete MACHINE-LEARNING project.
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Project's Overview
NeuroScan is a brain tumor detection web application developed using Python, Flask, Machine Learning, OpenCV, scikit-learn, and SQLite. This project is designed to classify brain MRI images and predict whether the scan indicates a tumor or no tumor, with support for multiclass classification as well.
The system provides a complete workflow from MRI image upload and prediction to result history, report generation, and admin-based model training. It is an ideal project for students and developers looking for a medical image classification project in Python or a Flask machine learning project for final year students.
This application runs completely on a local environment without using any third-party AI APIs, making it a practical and secure solution for learning medical image processing, Flask web development, and machine learning model deployment
Login Credentials
Default Admin Login
Username: admin
Password: Admin@123
Default User Login
Username: alice_c
Password: User@123
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- Secure admin login system with separate admin session
- Admin dashboard with total users, MRI uploads, predictions, and tumor vs no-tumor statistics
- User management system with edit, activate, deactivate, reset password, and delete options
- View and manage all uploaded brain MRI images
- Access all prediction records with filters and delete functionality
- Dataset management to upload training images into tumor and non-tumor folders
- Train and retrain the brain tumor detection machine learning model from the admin panel
- View model accuracy, classification report, and confusion matrix
- Generate downloadable HTML reports such as user-wise, date-wise, and overall system reports
- Manage user feedback and contact messages
- Update system settings like allowed file types, upload size, model path, and admin credentials
- User registration with name, email, username, password, and security question
- Secure login and logout using email or username
- Forgot password option using security answer verification
- User profile management with edit profile and change password functionality
- Upload brain MRI scan images in JPG, JPEG, and PNG formats
- Preview MRI images before prediction
- Run brain tumor prediction using the trained machine learning model
- View prediction result with class label, confidence score, timestamp, and image details
- Download prediction reports in PDF and TXT format
- Access prediction history with filters by date, tumor status, and label
- Delete personal prediction records
- User dashboard with total scans and recent prediction history
- Built with Flask web framework for a lightweight and scalable backend
- Uses SQLite database for storing users, predictions, feedback, and system settings
- Implements secure password hashing with Werkzeug
- Supports machine learning model training and inference locally
- Feature extraction using OpenCV, including HOG, histogram, image statistics, and edge detection
- Uses RandomForestClassifier from scikit-learn for classification
- Supports both binary classification and multiclass brain tumor detection
- Generates downloadable reports and visual model performance analytics
- Responsive frontend built with HTML, CSS, and Bootstrap 5
- Ideal for final year projects, portfolio websites, and machine learning project showcases
Step 1: Install Dependencies
Open the project folder and run:
pip install -r requirements.txt
Step 2: Seed Demo Data
To create the database, sample users, sample predictions, and demo model, run:
python seed_data.py
Step 3: Start the Flask Server
Run the application using:
python run.py
Step 4: Open in Browser
Visit:
http://127.0.0.1:5000
README Included
Setup Support Available
Default Admin Login
Username: admin
Password: Admin@123
Default User Login
Username: alice_c
Password: User@123