Breast Cancer Detection System

Early detection saves lives. This system predicts whether a tumor is benign or malignant using machine learning — built as a full-stack application so it is accessible to anyone, not just data scientists.

---

The Problem

Breast cancer is one of the most common cancers worldwide. Early and accurate detection significantly improves survival rates. Manual diagnosis is time-consuming and subject to human error. This system assists healthcare professionals by providing a fast, data-driven second opinion.

---

How It Works

Random Forest classifier trained on the Wisconsin Breast Cancer Dataset. Features include radius, texture, perimeter, area, and smoothness of cell nuclei. The model predicts benign or malignant with real-time results through a web interface.

Patient Data Input
↓
Feature Preprocessing + Scaling
↓
Random Forest Classifier
↓
Benign / Malignant Prediction + Confidence Score

---

Results

Metric	Score
Accuracy	95%+
Evaluation	Accuracy, Precision, ROC-AUC
Dataset	Wisconsin Breast Cancer Dataset (WBC)
Features	Radius, Texture, Perimeter, Area, Smoothness

---

Project Structure

Breast-Cancer-Detection/
│
├── notebook/        # Model training, EDA, feature engineering
├── backend/         # FastAPI backend - model inference and API
├── frontend/        # React frontend - user interface

---

Tech Stack

Machine Learning: Python, Scikit-learn, Pandas, NumPy, Jupyter

Backend: FastAPI

Frontend: React, HTML, CSS

Visualization: Matplotlib, Seaborn

---

Setup & Run

# Clone the repo
git clone https://github.com/aadityaKS1/Breast-Cancer-Detection
cd Breast-Cancer-Detection

# Backend
cd backend
pip install -r requirements.txt
uvicorn main:app --reload

# Frontend
cd frontend
npm install
npm start

Open http://localhost:3000 in your browser.

---

Dataset

Wisconsin Breast Cancer Dataset (WBC) - features extracted from digitized images of fine needle aspirate (FNA) of breast masses.

Features used:

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

---

Applications

• Clinical decision support for early breast cancer detection
• Healthcare AI research and education
• Demonstrating full-stack ML deployment in medical contexts

---

Author

Aaditya Kumar Singh LinkedIn - GitHub