Breast Cancer Detection Using Neural Networks

Overview

This project aims to develop a breast cancer detection model using neural networks implemented with TensorFlow and Keras. The model is designed to classify whether a tumor is benign or malignant based on medical imaging data.

Background

Breast cancer is one of the most common types of cancer among women worldwide. Early detection significantly increases the chances of successful treatment. This project leverages machine learning techniques to assist healthcare professionals in diagnosing breast cancer more effectively.

Technologies Used

• Python
• TensorFlow: An open-source library for machine learning.
• Keras: A high-level neural networks API running on top of TensorFlow.
• Pandas: For data manipulation and analysis.
• NumPy: For numerical computations.
• Matplotlib/Seaborn: For data visualization.
• Scikit-Learn: For data preprocessing and evaluation metrics. Breast Cancer Detection Using Neural Networks Overview This project aims to develop a breast cancer detection model using neural networks implemented with TensorFlow and Keras. The model is designed to classify whether a tumor is benign or malignant based on medical imaging data. Table of Contents - Background - Technologies Used - Dataset - Installation - Usage - Model Architecture - Results - Contributing - License Background Breast cancer is one of the most common types of cancer among women worldwide. Early detection significantly increases the chances of successful treatment. This project leverages machine learning techniques to assist healthcare professionals in diagnosing breast cancer more effectively. Technologies Used - Python - TensorFlow: An open-source library for machine learning. - Keras: A high-level neural networks API running on top of TensorFlow. - Pandas: For data manipulation and analysis. - NumPy: For numerical computations. - Matplotlib/Seaborn: For data visualization. - Scikit-Learn: For data preprocessing and evaluation metrics. Dataset The dataset used for this project is the Breast Cancer dataset, which is available from the scikit-learn library. Key Features of the load_breast_cancer Dataset: Purpose: The dataset is primarily used for the classification of tumors as either malignant (cancerous) or benign (non-cancerous) based on various attributes.

Data Structure: The dataset contains 30 features representing different characteristics of the tumors, such as:

Radius Texture Perimeter Area Smoothness Compactness Concavity Symmetry Fractal Dimension Target Variable: The target variable consists of binary labels:

0: Benign 1: Malignant Number of Samples: The dataset contains 569 samples (tumor measurements).

Samples: 569 - Features: 30 numerical features, including radius, texture, perimeter, area, smoothness, compactness, concavity, and symmetry. Installation To set up the project environment, follow these steps: 1. Clone the repository: bash git clone https://github.com/yourusername/breast-cancer-detection.git cd breast-cancer-detection 2. Install the required packages: bash pip install -r requirements.txt

Results The model achieves an accuracy of approximately 96% on the test set . This performance demonstrates the potential of using neural networks for breast cancer detection. Contributing Contributions are welcome! Please feel free to submit a pull request or open an issue to discuss any improvements.