Breast Cancer Detection Accuracy Evaluation

This repository contains multiple methods for assessing the accuracy score of breast cancer detection algorithms.

Introduction

Breast cancer detection is a critical area of research in medical imaging. Evaluating the accuracy of detection algorithms is essential to ensure their reliability and effectiveness in clinical settings. This repository provides various approaches for assessing the accuracy of breast cancer detection algorithms using different metrics and techniques.

Methods

The following methods are included in this repository for evaluating accuracy:

Confusion Matrix Analysis: Calculates true positives, true negatives, false positives, and false negatives to derive accuracy, precision, recall, and F1 score.
Cross-Validation: Utilizes k-fold cross-validation to assess the model's performance on multiple subsets of the data, providing a more robust estimation of accuracy.
Receiver Operating Characteristic (ROC) Curve: Plots the true positive rate against the false positive rate at various threshold settings and calculates the area under the ROC curve (AUC) as a measure of accuracy.
Precision-Recall Curve: Plots precision against recall at various threshold settings and calculates the area under the curve (AUC-PR) to evaluate the model's accuracy.
Leave-One-Out Cross-Validation (LOOCV): Evaluates the model's performance by training it on all but one data point and testing on the left-out data point, iterating over all data points.

Usage

To assess the accuracy of breast cancer detection algorithms using any of the methods mentioned above, follow these steps:

Clone the repository to your local machine:

git clone https://github.com/varungupta04/Breast_cancer.git

Navigate to the cloned directory:
```
cd ['to the desired model']
```
Choose the method you want to use for accuracy evaluation and follow the instructions provided in the respective directory.

Requirements

Python 3.x
Required Python libraries (NumPy, Pandas, Matplotlib, Scikit-learn, etc.)

Contributors

Varun Gupta
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License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

This repository was inspired by the need for standardized methods to evaluate breast cancer detection algorithms.
We thank the open-source community for their valuable contributions to the tools and libraries used in this project.

Feedback and Contributions

Feedback, bug reports, and contributions are welcome! Please feel free to submit issues, pull requests, or contact the project maintainers. We appreciate your support in improving this repository.