Breast Cancer Detection System Using Machine Learning

A web application that leverages machine learning to detect breast cancer by analyzing medical data inputs and classifying cases as either benign or malignant with high accuracy.

📋 Project Overview

This system uses the Wisconsin Breast Cancer Dataset to train machine learning models that can distinguish between benign and malignant breast cancer cases. The application provides an easy-to-use interface for medical professionals to input patient data and receive instant predictions.

✨ Features

• High Accuracy: Achieves ~90% accuracy in cancer detection
• Input Preprocessing: Implements feature scaling for consistent model performance
• User-friendly Interface: Intuitive web application for medical data input
• Automated Testing: Includes Selenium-based testing for application functionality
• Responsive Design: Clean CSS styling for various device sizes

🧪 Dataset

The system is trained on the Wisconsin Breast Cancer Dataset, which contains 30 features computed from digitized images of fine needle aspirates (FNA) of breast mass. Features include:

• Radius, texture, perimeter, area, smoothness
• Compactness, concavity, concave points
• Symmetry, fractal dimension
• Mean, standard error, and "worst" values for each feature

The target variable is coded as:

• 0: Malignant
• 1: Benign

🔧 Technologies Used

• Backend: Python, Flask
• Machine Learning: scikit-learn
• Data Processing: NumPy, joblib for model persistence
• Frontend: HTML5, CSS3
• Testing: Selenium WebDriver for automated testing

Folder Explanations

• model/: Stores the trained machine learning model and scaler for preprocessing.
• static/: Contains static files like CSS for frontend styling.
• templates/: Holds HTML templates for rendering web pages.
• utils/: Utility scripts, such as data preprocessing functions.
• testing/: Automated test scripts (e.g., Selenium) for end-to-end testing.
• app.py: Main Flask application entry point.
• breast_cancer_dataframe.csv: The dataset used for model training and evaluation.

🚀 Installation and Setup

# Clone the repository
git clone https://github.com/Divak-ar/Breast_Cancer.git

# Navigate to the project directory
cd breast_cancer

# Install dependencies
pip install -r requirements.txt

# Run the application
python app.py

## 💻 Usage

1. Launch the web application using `python app.py`
2. Open your browser and go to `http://127.0.0.1:5000`
3. Enter patient data in the provided input fields
4. Click the "Predict" button to receive cancer classification 
5. The system will display whether the sample is likely benign or malignant

## 🔍 Testing

Automated testing using Selenium WebDriver is included to ensure the application works as expected:

```bash
# Run automated tests
python testing/test_app.py

The test script automatically:

• Launches the web application
• Inputs test data into the form fields
• Submits the form and validates the prediction response
• Provides test results in the console output