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https://github.com/alihassanml/breast-cancer-classfication
This repository contains a deep learning-based classification model for predicting breast cancer using a neural network. The model is trained on the Wisconsin Breast Cancer dataset and can classify whether a tumor is malignant or benign based on various features.
https://github.com/alihassanml/breast-cancer-classfication
breastcancer-classification deep-learning neural-network project
Last synced: 8 days ago
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This repository contains a deep learning-based classification model for predicting breast cancer using a neural network. The model is trained on the Wisconsin Breast Cancer dataset and can classify whether a tumor is malignant or benign based on various features.
- Host: GitHub
- URL: https://github.com/alihassanml/breast-cancer-classfication
- Owner: alihassanml
- License: mit
- Created: 2024-08-12T07:56:55.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-08-12T08:03:53.000Z (5 months ago)
- Last Synced: 2024-11-08T17:16:03.210Z (2 months ago)
- Topics: breastcancer-classification, deep-learning, neural-network, project
- Language: Jupyter Notebook
- Homepage:
- Size: 110 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Breast Cancer Classification
This repository contains a deep learning-based classification model for predicting breast cancer using a neural network. The model is trained on the Wisconsin Breast Cancer dataset and can classify whether a tumor is malignant or benign based on various features.
## Table of Contents
- [Overview](#overview)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Model](#model)
- [Results](#results)
- [Streamlit Application](#streamlit-application)
- [Contributing](#contributing)
- [License](#license)
## Overview
Breast cancer is one of the most common cancers among women. Early detection through classification can significantly improve treatment outcomes. This project uses a neural network to classify breast cancer tumors as either malignant or benign based on several features extracted from cell nuclei present in digitized images of a fine needle aspirate (FNA) of a breast mass.
## Features
- **Deep Learning Model**: A neural network trained for high accuracy classification.
- **Preprocessing**: Data is scaled using a pre-trained scaler to improve model performance.
- **Streamlit Web App**: An interactive web application built with Streamlit to allow users to input features and get predictions.
## Installation
To run this project locally, follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/alihassanml/Breast-Cancer-Classification.git
cd Breast-Cancer-Classification
```
2. Install the required dependencies:
```bash
pip install -r requirements.txt
```
3. Make sure the following files are in the project directory:
- `model.h5`: The pre-trained neural network model.
- `scalar.pkl`: The scaler used for feature scaling.
## Usage
### Running the Streamlit App
You can run the Streamlit app to make predictions using the trained model:
```bash
streamlit run app.py
```
### Input Features
The app requires the following features to make a prediction:
- Mean Radius
- Mean Texture
- Mean Perimeter
- Mean Area
- Mean Smoothness
- Mean Compactness
- Mean Concavity
- Mean Concave Points
- Mean Symmetry
- Mean Fractal Dimension
- Radius Error
- Texture Error
- Perimeter Error
- Area Error
- Smoothness Error
- Compactness Error
- Concavity Error
- Concave Points Error
- Symmetry Error
- Fractal Dimension Error
- Worst Radius
- Worst Texture
- Worst Perimeter
- Worst Area
- Worst Smoothness
- Worst Compactness
- Worst Concavity
- Worst Concave Points
- Worst Symmetry
- Worst Fractal Dimension
### Model Prediction
The model outputs a prediction indicating whether the tumor is likely to be malignant or benign.
## Model
The neural network model is built using TensorFlow and Keras. It has been trained on the Wisconsin Breast Cancer dataset, achieving an accuracy of 98.40% on the test set.
## Results
### Confusion Matrix:
```
[[ 66 1]
[ 2 119]]
```
### Classification Report:
```
precision recall f1-score support
0 0.97 0.99 0.98 67
1 0.99 0.98 0.99 121
accuracy 0.98 188
macro avg 0.98 0.98 0.98 188
weighted avg 0.98 0.98 0.98 188
```
## Streamlit Application
The Streamlit application provides a user-friendly interface to input the necessary features and receive a prediction. The app is designed for easy deployment and use.
## Contributing
Contributions are welcome! If you have any suggestions, feel free to open an issue or create a pull request.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.