Brain Tumor Detection System using Python and Machine Learning Source Code ( Final Year)
Download clean, well-commented Brain Tumor Detection System using Python and Machine Learning source code for final year projects — easy to run, demo-ready, and mentor-friendly.
- MACHINE-LEARNING Project
- MySQL / MongoDB
- Setup guide & demo steps
- Beginner-friendly
Keywords: source code, final year project code, Brain Tumor Detection System using Python and Machine Learning Git, documentation, installation guide, machine-learning project, college project demo.
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Admin Features
- Secure admin login with separate session handling
- Admin dashboard with total users, uploads, predictions, tumor and no-tumor statistics
- Manage users with search, edit, activate, deactivate, reset password, and delete options
- View all uploaded MRI images with user details and upload date
- Manage all prediction records with filters by class or tumor status
- Dataset management to upload images into yes, no, or multiclass folders
- Train and retrain machine learning model from dataset folders
- View model performance, accuracy, classification report, and confusion matrix
- Download HTML reports including user-wise, date-wise, and system-wide reports
- View and delete contact/feedback messages
- Update system settings like allowed file types, max upload size, and model path
Description
NeuroScan is a college-level brain tumor detection web application built with Python Flask, SQLite, scikit-learn, and OpenCV. The system allows users to upload brain MRI images, preview scans, run brain tumor classification, and download PDF/TXT reports. It includes a complete user panel and admin dashboard with authentication, prediction history, model training, dataset management, reporting, and feedback handling.
This project performs local machine learning inference without using any third-party API. The ML pipeline uses HOG features, image histogram analysis, edge detection, and a RandomForestClassifier for MRI image classification. It supports both binary classification such as Tumor / No Tumor and multiclass brain tumor classification depending on the training dataset.
The project is ideal for final year students, machine learning beginners, Flask web development learners, and academic demonstration purposes
The Brain Tumor Detection System using Python and Machine Learning final-year Brain Tumor Detection System using Python and Machine Learning source code is structured for fast setup and easy customization. You get readable code, clear folder architecture, and a guided README so you can run locally and present confidently.
Source Code Overview
Technical snapshot & environment- Project Name
- Brain Tumor Detection System using Python and Machine Learning
- Language / Stack
- machine-learning
- Database
- MySQL or MongoDB
- Browsers
- Chrome, Firefox, Edge, Opera
- Included in the download
- Frontend,Backend,Database
- Run Scripts
- Documented in README (install, seed, start)
- License
- Academic use for college submission
User Features
- User registration with name, email, username, password, and security question
- Login and logout using email or username
- Forgot password using security answer verification
- User profile management to edit account details and change password
- Upload brain MRI images in JPG, JPEG, or PNG format
- Preview MRI image before prediction
- Run brain tumor prediction locally using trained ML model
- View prediction result with class label, confidence score, image, and timestamp
- Download PDF and TXT reports for results
- Access prediction history with date filter, label filter, search, detail view, and delete option
- User dashboard with summary statistics and recent predictions
Other Features
- Secure password hashing using Werkzeug
- Separate user and admin session management
- Server-side form and file validation
- SQLite database for storing users, predictions, feedback, and settings
- Local model inference without cloud API dependency
- OpenCV feature extraction using HOG, histogram, grayscale statistics, and edge density
- RandomForestClassifier for efficient MRI image classification
- Supports binary and multiclass classification
- Generates training metrics JSON and confusion matrix image
- Ideal for college project submission, portfolio showcase, and academic demonstration
How to run Brain Tumor Detection System using Python and Machine Learning
- Install Python 3.10+ and pip
- Open the project folder in terminal
- Install dependencies using:
pip install -r requirements.txt
- Optionally seed demo data using:
python seed_data.py
- Start the Flask server:
python run.py
- Open in browser:
http://127.0.0.1:5000
Credentials
Default Admin Login
- Username: admin
- Password: Admin@123
Default User Login
- Username: alice_c
- Password: User@123
License
Brain Tumor Detection System using Python and Machine Learning Source Code Tags
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