TapuSau/breast-cancer-detection

🩺 Breast Cancer Diagnostic System

A Machine Learning-based web application built with Streamlit that predicts whether a breast tumor is Benign or Malignant using clinical measurement data.

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📌 Project Overview

This project uses a Decision Tree Classifier trained on breast cancer diagnostic data to help analyze tumor characteristics and predict cancer severity.

The application provides a simple and interactive user interface where users can enter clinical measurements and instantly receive prediction results.

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🚀 Features

• Interactive Streamlit web interface
• Real-time tumor diagnosis prediction
• Decision Tree Machine Learning model
• User-friendly clinical input form
• Instant result visualization
• Lightweight and easy to run

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🛠️ Technologies Used

• Python
• Streamlit
• Pandas
• NumPy
• Scikit-learn

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📂 Project Structure

breast-cancer-diagnostic-system/
│
├── app.py
├── data.csv
├── requirements.txt
├── README.md
└── screenshots/

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📊 Dataset Information

The dataset contains breast cancer clinical measurements such as:

• Radius
• Texture
• Perimeter
• Area
• Smoothness
• Compactness
• Symmetry
• Fractal Dimension

Target Labels:

• M → Malignant (Cancerous)
• B → Benign (Non-Cancerous)

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⚙️ Installation

1️⃣ Clone Repository

git clone https://github.com/YOUR_USERNAME/breast-cancer-diagnostic-system.git

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2️⃣ Navigate to Project Folder

cd breast-cancer-diagnostic-system

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3️⃣ Install Required Libraries

pip install -r requirements.txt

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▶️ Run Application

streamlit run app.py

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🖥️ Application Workflow

1. Enter patient clinical measurements
2. Click Analyze Results
3. System predicts:
   • Malignant
   • Benign

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📸 Screenshots

Add application screenshots inside:

screenshots/

Example:

• Home Page
• Prediction Result
• Input Form

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📈 Machine Learning Model

Model Used:

Decision Tree Classifier

Reason for selection:

• Fast training
• Easy interpretability
• Good performance on structured datasets

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📌 Future Improvements

• Add multiple ML algorithms
• Improve UI design
• Add model accuracy visualization
• Deploy on Streamlit Cloud
• Add patient report export feature

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👨‍💻 Author

Tapabrata Sau

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📄 License

This project is licensed under the MIT License.