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BSc Project Report
BSc Brain Tumor Detection System Using Machine Learning Report
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Abstract
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Table of Content
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01
Introduction
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02
Problem Statement
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03
Existing System
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04
Proposed System
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05
Objectives
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06
System Architecture
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07
Major Functional Modules
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08
Hardware Requirements
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09
Software Requirements
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10
Future Enhancement
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11
Conclusion
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12
References
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Abstract
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Table of Content
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01
Chapter 1 — Introduction
- 1.1 Background of the Project
- 1.2 Problem Statement
- 1.3 Objectives of the System
- 1.4 Scope of the Project
- 1.5 Existing System Overview
- 1.6 Proposed System Overview
- 1.7 Technologies Used
- 1.8 Overview
- 1.9 Limitations
- 1.10 Advantages
- 1.11 Disadvantages
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02
Chapter 2 — Literature Review / System Study
- 2.1 Introduction
- 2.2 Review of Similar Systems
- 2.3 Comparative Analysis
- 2.4 Software Development Models
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03
Chapter 3 — System Analysis
- 3.1 Introduction
- 3.2 Functional Requirements
- 3.3 Non-Functional Requirements
- 3.4 User Requirements
- 3.5 Feasibility Study
- 3.5.1 Technical Feasibility
- 3.5.2 Economic Feasibility
- 3.5.3 Operational Feasibility
- 3.6 System Architecture
- 3.7 Data Flow Diagram (Level 0)
- 3.8 Data Flow Diagram (Level 1)
- 3.9 Data Flow Diagram (Level 2)
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04
Chapter 4 — System Design
- 4.1 Introduction
- 4.2 Use Case Diagram
- 4.3 Class Diagram
- 4.4 Sequence Diagram
- 4.5 Activity Diagram
- 4.6 ER Diagram
- 4.7 Database Schema
- 4.8 Table Structures
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05
Chapter 5 — System Implementation
- 5.1 Introduction
- 5.2 Development Environment
- 5.3 Tools and Technologies Used
- 5.4 Hardware Requirements
- 5.5 Software Requirements
- 5.6 Module-wise Implementation
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06
Chapter 6 — Testing
- 6.1 Introduction
- 6.2 Testing Strategy
- 6.3 Unit Testing
- 6.4 Integration Testing
- 6.5 System Testing
- 6.6 Test Cases
- 6.7 Bug Reports
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07
Chapter 7 — Results and Discussion
- All screenshots
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08
Chapter 8 — Conclusion and Future Enhancements
- 8.1 Conclusion
- 8.2 Future Enhancements
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09
Chapter 9 — References
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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- Report
- Synopsis
- Diagrams
- 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