Multilayer-Perceptron 42

Breast Cancer Classification with Neural Networks

Overview

This project implements a multilayer perceptron (neural network) to classify breast cancer as either malignant or benign using the Wisconsin Breast Cancer dataset. The implementation includes a complete neural network framework with various components for training, evaluation, and visualization.

Dataset

The project uses the Wisconsin Breast Cancer Dataset which contains:

• Features computed from breast mass images
• Binary classification: Malignant (M) or Benign (B)
• 30 features including radius, texture, perimeter, area, smoothness, etc.
• Each row represents a single case with corresponding measurements

Requirements

numpy matplotlib pandas scikit-learn seaborn

Project Structure

├── data/
│   └── data.csv         # Wisconsin Breast Cancer Dataset
├── srcs/
│   ├── model/
│   │   ├── activation.py    # Activation functions (ReLU, Sigmoid, Softmax)
│   │   ├── layer.py        # Neural network layers
│   │   ├── loss.py         # Loss functions
│   │   ├── metrics.py      # Evaluation metrics
│   │   ├── neural_network.py # Main neural network implementation
│   │   ├── optimizers.py   # Optimization algorithms (Adam, RMSProp)
│   │   └── preprocess.py   # Data preprocessing utilities
│   └── programs/
│       └── multiple_model_train.py # Training script for multiple models

Features

Neural Network Components

• Layers: Dense layers with customizable neurons

• Activation Functions:

  • ReLU
  • Sigmoid
  • Softmax

• Optimizers:

  • Adam
  • RMSProp
  • SGD

• Loss Functions:

  • Binary Cross-Entropy
  • Mean Squared Error
  • Categorical Cross-Entropy

Training Features

• Batch training support
• Early stopping with patience
• Learning rate decay
• Validation set evaluation
• Model saving and loading

### Evaluation Metrics

• Accuracy
• Precision
• Recall
• F1 Score
• Confusion Matrix

Visualization

• Training history plots
• Learning curves
• Confusion matrix visualization

Usage

1. Install dependencies:

make virtual
make create # Create data, depo and predictions folders

2. Prepare the dataset:

python programs/preprocess.py --input path/to/dataset.csv

3. Train multiple models:

python programs/multiple_model_train.py --train path/to/train.csv --valid path/to/valid.csv --epochs 50 --batch_size 8 --learning_rate 0.005

4. Use the trained model for predictions:

python programs/predict.py --model path/to/model.npy --input path/to/valid.csv --output path/to/predictions.csv

Performance Metrics

The model evaluates performance using:

• Confusion Matrix
• Precision, Recall, and F1 Score
• Training and Validation Accuracy/Loss Curves

Contributing

Feel free to submit issues and enhancement requests.