Breast-Cancer-Detection-model

Model Description:

This model leverages Logistic Regression to analyze features extracted from mammogram images and classify them as either benign or malignant. The model is trained on a dataset containing these features alongside labels indicating the presence or absence of cancer.

Features:

Feature Extraction:

1. Texture features

2. Shape features

3. Density features

Data Preprocessing:

Standardize or normalize the extracted features to ensure they are on a similar scale.

Model Training:

Train the Logistic Regression model on the dataset of extracted features and corresponding labels.

Evaluation:

Evaluate the model's performance on a separate test set using metrics like accuracy, precision, recall, and F1 score.

Benefits:

Logistic Regression provides interpretable results, allowing us to understand which features contribute most to the model's predictions.

Simplicity: Logistic Regression is a relatively simple model, making it easier to train and interpret compared to complex deep learning architectures.

Clone this repository.

1. Install the required dependencies (listed in a requirements.txt file).

2. Download a dataset containing mammogram features and labels (not included in this repository due to data sensitivity).

3. Configure the training script with your dataset paths and hyperparameters.

4. Run the training script to train the model.

5. Evaluate the trained model on a separate test set.

Additional Notes:

1. Feel free to contribute to this project by exploring different feature extraction techniques or adding improvements to the training process and model selection.

2. Include clear instructions and comments in the code to enhance understanding and maintainability.

3. While Logistic Regression offers a simpler approach, more complex models like deep learning might achieve higher accuracy. This codebase provides a foundation for you to develop and experiment with machine learning models for breast cancer detection using Logistic Regression. Remember, thorough data exploration, feature engineering, and model evaluation are crucial for building a robust model.

Disclaimer:

This model is for educational purposes only and should not be used for medical diagnosis. It is important to consult with a qualified healthcare professional for any questions or concerns about breast cancer.