🧬 Cancer Detection (Breast & Prostate MRI)

This project demonstrates a full-stack ML deployment workflow, focusing on prostate and breast cancer detection from MRI images using 3D U-Net and other deep learning architectures.

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🚀 Features

• Deep Learning Models
  • 3D U-Net for prostate cancer detection
  • CNN-based model for breast cancer detection
• Backend
  • Flask REST API for model inference
• Frontend
  • React.js interface for uploading MRI scans and viewing predictions
• End-to-End Flow
  • Upload → Preprocess → Predict → Display results

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📂 Project Structure

cancer-detection/ │── backend/ # Flask API code │ ├── app.py # Main API script │ ├── models/ # Saved ML models │ └── utils/ # Preprocessing & helper functions │ │── frontend/ # React.js frontend │ ├── src/ # React source code │ └── public/ # Static assets │ │── datasets/ # MRI datasets (not included in repo) │── requirements.txt # Python dependencies │── README.md # Project documentation

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⚙️ Installation

1️⃣ Clone the Repository

git clone https://github.com/your-username/cancer-detection.git

cd cancer-detection

2️⃣ Backend Setup (Flask + PyTorch)

cd backend

python -m venv .venv

source .venv/bin/activate # (Linux/Mac)

.venv\Scripts\activate # (Windows)

pip install -r requirements.txt

Run the Flask server:

python app.py

3️⃣ Frontend Setup (React.js)

cd frontend

npm install

npm start

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🖥️ Usage

• Start the Flask backend.
• Run the React frontend.
• Upload an MRI scan via the UI.
• Get predictions for cancer presence (positive/negative).

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📦 Requirements

All Python dependencies are listed in requirements.txt. Key libraries:

• PyTorch
• Flask
• scikit-learn
• numpy, pandas
• opencv-python
• matplotlib
• React.js (frontend)