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https://github.com/harshjuly12/deep-learning-approaches-for-enhanced-breast-cancer-detection
A deep learning project aimed at early detection of breast cancer by classifying tumors as benign or malignant based on features extracted from cell images. The project demonstrates data preprocessing, model training, and evaluation using various deep learning algorithms to achieve high accuracy in predictions.
https://github.com/harshjuly12/deep-learning-approaches-for-enhanced-breast-cancer-detection
aiml cnn deep-learning deep-neural-networks
Last synced: about 2 months ago
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A deep learning project aimed at early detection of breast cancer by classifying tumors as benign or malignant based on features extracted from cell images. The project demonstrates data preprocessing, model training, and evaluation using various deep learning algorithms to achieve high accuracy in predictions.
- Host: GitHub
- URL: https://github.com/harshjuly12/deep-learning-approaches-for-enhanced-breast-cancer-detection
- Owner: harshjuly12
- License: mit
- Created: 2024-06-25T17:21:10.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-07-16T08:59:56.000Z (6 months ago)
- Last Synced: 2024-07-16T11:26:32.267Z (6 months ago)
- Topics: aiml, cnn, deep-learning, deep-neural-networks
- Language: Jupyter Notebook
- Homepage:
- Size: 5.97 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
Breast Cancer Detection Using Deep Learning
## Table of Contents
1. [Introduction](#introduction)
2. [Dataset](#dataset)
3. [Features](#features)
4. [Technologies Used](#technologies-used)
5. [Installation](#installation)
6. [Usage](#usage)
7. [Example Output](#example-output)
8. [Contributing](#contributing)
9. [License](#license)
10. [Author](#author)
## Introduction
Breast cancer is one of the most common types of cancer affecting women worldwide. Early detection and diagnosis are crucial for effective treatment and improving survival rates. This project aims to assist in the early detection of breast cancer using machine learning algorithms. By analyzing various features extracted from cell images, the model can classify tumors as benign or malignant with high accuracy. The goal is to provide a reliable tool that can aid medical professionals in making more informed decisions.
This project demonstrates the end-to-end process of building a machine learning model, from data preprocessing and feature selection to model training, evaluation, and deployment. Various machine learning algorithms are implemented and compared to identify the best-performing model for this classification task.
## Dataset
The dataset used in this project is the Breast Cancer Wisconsin (Diagnostic) dataset. It consists of 569 samples, each with 30 features, including mean, standard error, and worst (mean of the three largest values) of ten real-valued features computed for each cell nucleus.
## Features
- Data preprocessing and normalization
- Implementation of various machine learning models
- Evaluation of model performance
- Visualization of results
## Technologies Used
- Python 3.x
- Jupyter Notebook
- Scikit-learn
- Pandas
- Numpy
- Matplotlib
- Seaborn
## Installation
1. **Clone the repository:**
```bash
git clone https://github.com/your-username/Breast-Cancer-Detection.git
cd Breast-Cancer-Detection
```
2. **Create a virtual environment:**
```bash
python -m venv venv
```
3. **Activate the virtual environment:**
- On Windows:
```bash
venv\Scripts\activate
```
- On macOS/Linux:
```bash
source venv/bin/activate
```
4. **Install the required packages:**
```bash
pip install -r requirements.txt
```
## Usage
1. **Open the Jupyter Notebook:**
```bash
jupyter notebook Breast_Cancer_Detection.ipynb
```
2. **Run the cells in the notebook to preprocess the data, train the models, and evaluate the results.**
## Example Output
Here are some example outputs from the project:
Accuracy of SVM model: 98.2%
Confusion Matrix:
[ [102 3]
[2 63] ]
## Contributing
Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Author
For any questions or suggestions, please contact:
- Harsh Singh: [[email protected]]([email protected])
- GitHub: [harshjuly12](https://github.com/harshjuly12)