Breast Cancer Detection with Deep Neural Network

Welcome to the Breast Cancer Detection project, a deep learning-based system for detecting breast cancer malignancy using the Wisconsin Breast Cancer Diagnostic (WBCD) dataset. This project leverages deep neural networks to achieve accurate and efficient cancer diagnosis.

Table of Contents

• About
• Key Features
• How It Works
• Getting Started
• Usage
• Accuracy Metrics

About

This project is designed to aid in the early detection of breast cancer, leveraging state-of-the-art deep neural networks and a high-quality dataset. It provides healthcare professionals with a powerful tool for more accurate and efficient cancer diagnosis.

Key Features

• Deep Neural Network: Utilizes a deep learning architecture for feature extraction and malignancy prediction.

• Wisconsin Breast Cancer Dataset: Analyzes patient data from the WBCD dataset, a widely recognized and reliable source for breast cancer diagnosis.

• Accurate Predictions: Achieves high accuracy in breast cancer malignancy predictions, reducing the risk of misdiagnosis.

• User-Friendly Interface: Provides an easy-to-use interface for healthcare professionals and data scientists.

How It Works

The project uses deep neural networks to analyze patient data from the Wisconsin Breast Cancer Diagnostic dataset. It preprocesses the data, extracts meaningful features, and uses these features to predict the malignancy of breast tumors.

Getting Started

To start using this breast cancer detection system, follow these steps:

1. Clone the repository to your local machine.

2. Install the necessary dependencies, including Python, TensorFlow, and other required libraries. You can refer to the requirements.txt file.

3. Explore the codebase to understand the neural network architecture and data processing.

4. Run the system on your local machine and begin making breast cancer predictions.

Usage

To use the system for breast cancer detection, you can integrate it into your application or use it as a standalone diagnostic tool. Detailed usage instructions can be found in the project's documentation.

Accuracy Metrics

Accuracy is a critical factor in medical diagnosis. This project employs a range of accuracy metrics, including but not limited to:

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

These metrics are used to evaluate the system's performance in breast cancer malignancy detection.