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MACHINE-LEARNING Project
Brain Tumor Detection System Using Machine Learning Source Code
Complete final-year project source code with frontend, backend, database and setup documentation. Instant download after secure payment.
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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
- 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