🧠💉 AI-Physician-for-Cancer – Your Virtual Oncology Assistant

Welcome to AI-Physician-for-Cancer!
An AI-powered virtual physician that helps detect malignant (cancerous) and benign tumors with deep learning.
This intelligent assistant brings the power of modern medicine and artificial intelligence together in a mission to save lives. ❤️

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🧬 What Is It?

🔎 AI-Physician-for-Cancer is a deep learning project that classifies breast cancer tumors as either:

• 1 → Malignant
• 0 → Benign

It uses a dense neural network trained on medical data to assist doctors, researchers, and students in identifying cancer risk from diagnostic features extracted from biopsies.

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📦 Tech Stack

Tool/Library	Purpose
🐍 Python	Main programming language
📊 Pandas	Data handling and manipulation
🔢 NumPy	Numerical operations
🤖 TensorFlow	Deep learning & model training
🧪 scikit-learn	Data preprocessing & evaluation

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📁 Dataset Info

🔗 Source: Breast Cancer Wisconsin (Diagnostic) Data Set
📄 File: cancer.csv

Features:

• 30 numerical input features (e.g., radius, texture, smoothness)
• 1 binary label column: diagnosis(1=m, 0=b)

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🧠 Model Architecture

Input Layer (30 features)
        ↓
Dense Layer (256 neurons, ReLU)
        ↓
Dense Layer (256 neurons, ReLU)
        ↓
Output Layer (1 neuron, Sigmoid)

✅ Loss Function: binary_crossentropy
⚙️ Optimizer: adam
📈 Metrics: accuracy (can add precision/recall)

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🚀 Getting Started

🔧 Setup Instructions

1. Clone the repo:

   git clone https://github.com/your-username/AI-Physician-for-Cancer.git
   cd AI-Physician-for-Cancer

2. Install dependencies:

   pip install pandas tensorflow scikit-learn

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📊 Model Evaluation

After training, the model will:

• ✅ Output test accuracy
• 🔍 Provide insights on performance
• 🧠 Be ready for prediction and deployment

📌 Add this to improve evaluation:

from sklearn.metrics import classification_report, confusion_matrix

# Predict
y_pred = (model.predict(x_test) > 0.5).astype("int32")

# Evaluate
print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))

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🎯 Features To Add (Next Steps)

✅ Normalize the dataset using StandardScaler
✅ Use ReLU instead of Sigmoid for hidden layers
🔄 Add EarlyStopping and ModelCheckpoint callbacks
📈 Log metrics using TensorBoard
🖥️ Build a GUI or Web App using Streamlit or Flask
☁️ Deploy model as a REST API (e.g., FastAPI + Heroku)

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⚠️ Disclaimer

⚠️ Not for clinical use!
This tool is for educational and research purposes only.
It is not intended to replace medical diagnosis or professional advice. Always consult certified healthcare professionals.

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👨‍⚕️ Author

👤 Mohamed Ayoub Essalami
🎓 Physics & Engineering Enthusiast | Passionate about AI in Healthcare
📬 Reach me on LinkedIn or via GitHub

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🧠 Inspiration

Inspired by the fusion of:

• 🧬 Medical science
• 🖥️ Artificial Intelligence
• 💡 The pursuit of saving lives through innovation

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📜 License

This project is licensed under the MIT License – see the LICENSE file for details.

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⭐️ Show Your Support

If you found this project helpful or interesting:

• ⭐️ Star the repo
• 🍴 Fork it
• 📢 Share it

Let’s use AI for good. Together. ❤️