https://github.com/freedisch/ml-protocol

https://github.com/freedisch/ml-protocol

Last synced: 19 days ago
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• Host: GitHub
• URL: https://github.com/freedisch/ml-protocol
• Owner: Freedisch
• Created: 2024-05-12T18:40:06.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-11-12T15:30:33.000Z (2 months ago)
• Last Synced: 2024-11-12T16:34:51.248Z (2 months ago)
• Language: Jupyter Notebook
• Size: 7.08 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Breast Cancer Image Classification using Transfer Learning

## Dataset

The dataset contains images related to bread cancer. Each image has a corresponding mask image.

## Pre-trained Models

We selected the following pre-trained models:

- **VGG16**: Known for its simplicity and effectiveness in image classification tasks.

- **ResNet50**: Utilizes residual learning, which allows training of very deep networks.

- **InceptionV3**: Combines multiple filter sizes and network-in-network architectures, providing high accuracy.

## Fine-Tuning

We fine-tuned the models by modifying the top layers and training them on the dataset. The following layers were modified:

- **GlobalAveragePooling2D**: To reduce the feature maps.

- **Dense (1024 units)**: Added for high-level features.

- **Dense (1 unit)**: Output layer for binary classification with sigmoid activation.

## Usage

To use this project:

1. Clone the repository to your local machine.

2. Ensure that the dataset is stored at the correct path, as specified in the notebook.

3. Open the `Transfer_Learning_Assignment.ipynb` notebook in Jupyter or another IPython notebook viewer.

4. Run the cells sequentially to perform the data preprocessing, model training, and evaluation.

## Evaluation Metrics

The models are evaluated based on the following metrics:

- Accuracy

- Loss

- Precision

- Recall

- F1 Score

These metrics are crucial for assessing the performance of the models, particularly in a medical context where accuracy and reliability are paramount.

## Results

The results section will contain tables and charts generated from the notebook, providing a visual and quantitative comparison of the performance of the different models.

## Contributions

Contributions to this project are welcome. You can contribute by improving the model training pipeline, introducing new models, or enhancing the data preprocessing steps.

## License

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