The "Breast Cancer Classification using Neural Networks" project focuses on predicting the presence of breast cancer using deep learning techniques. By leveraging popular Python libraries such as NumPy, Pandas, Scikit-learn, Matplotlib, and implementing neural networks, this project provides a comprehensive solution for accurate cancer classification.
The "Breast Cancer Classification using Neural Networks" project aims to develop a model that can accurately classify breast cancer as malignant or benign based on various features. Early detection and accurate classification of breast cancer are crucial for timely treatment and improved patient outcomes. By employing deep learning algorithms and a curated dataset, this project offers a valuable tool for breast cancer classification.
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Data Collection and Processing: The project involves collecting a dataset containing features extracted from breast cancer cell images, such as cell size, shape, and texture. Using Pandas, the collected data is cleaned, preprocessed, and transformed to ensure it is suitable for analysis. The dataset is included in the repository for easy access.
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Data Visualization: The project utilizes data visualization techniques to gain insights into the dataset. Matplotlib is employed to create visualizations such as histograms, bar plots, and scatter plots. These visualizations provide a deeper understanding of the distribution of features and help identify differences between malignant and benign samples.
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Train-Test Split: To evaluate the performance of the neural network model, the project employs the train-test split technique. The dataset is divided into training and testing subsets, ensuring that the model is trained on a portion of the data and evaluated on unseen data. This allows for an accurate assessment of the model's ability to generalize to new samples.
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Neural Network Model: The project utilizes a neural network architecture to build the classification model. Deep learning techniques, such as multi-layer perceptron (MLP), are employed to learn complex patterns and relationships within the dataset. The Scikit-learn library provides an implementation of MLP that is used in this project.
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Model Evaluation: The project evaluates the performance of the neural network model using evaluation metrics such as accuracy, precision, recall, and F1 score. These metrics provide insights into the model's ability to correctly classify malignant and benign breast cancer samples. Additionally, visualizations such as confusion matrices are created to compare the predicted labels against the actual labels.
To run this project locally, follow these steps:
- Clone the repository:
gh repo clone MYoussef885/Breast_Cancer_Classification_using_NN - Install the required libraries:
If you're using Google Colab, you don't need to pip install. Just follow the importing the dependencies section. - Launch Google Colab:
https://colab.research.google.com/ - Open the
Breast_Cancer_Classification.ipynbfile and run the notebook cells sequentially.
The "Breast Cancer Classification using Neural Networks" project offers a practical solution for accurately classifying breast cancer samples as malignant or benign. By leveraging data collection, preprocessing, visualization, and deep learning modeling, this project provides a comprehensive approach to addressing the classification task. The project also includes a curated dataset to facilitate seamless exploration and experimentation.
This project is licensed under the MIT license. See the LICENSE file for more information.
This project is made possible by the contributions of the open-source community and the powerful libraries it provides, including NumPy, Pandas, Scikit-learn, and Matplotlib. I extend my gratitude to the developers and maintainers of these libraries for their valuable work. In addition, the mentor Siddhardan, visit his channel here : https://www.youtube.com/@Siddhardhan