Prerequisites

Before running the project, ensure that you have the following dependencies installed:

• Python (version 3.7 or higher)
• Jupyter Notebook
• TensorFlow (version 2.0 or higher)
• Keras (version 2.4 or higher)
• NumPy
• SciPy
• Matplotlib

Getting Started

1. Clone the project repository to your local machine.
2. Navigate to the project directory.

Fine-Tuning the Diffusion Model

To create a new fine-tuned model and save its weights for future use, follow these steps:

1. Open DiffusionFineTuning.ipynb in Jupyter Notebook.
2. Run the notebook to perform the fine-tuning process.
3. Save the generated model weights for later use.

Generating Artificial Images

To generate artificial images using the fine-tuned diffusion model and save them to a .mat file, perform the following:

1. Open generateArtificialImages.ipynb in Jupyter Notebook.
2. Modify the path of the model weights to match the path of the previously generated model weights.
3. Run the notebook to generate the artificial images.
4. Save the generated images to a .mat file.

Running the Simple CNN

To execute a simple CNN without data augmentation techniques, use the following steps:

1. Open mainCNN.py in a Python IDE or editor.
2. Run the script to execute the simple CNN.

Running the CNN with Artificial Images

To run a CNN with the newly generated artificial images, follow these steps:

1. Open ArtificialCNN.py in a Python IDE or editor.
2. Modify the path to the generated .mat file obtained from generateArtificialImages.ipynb.
3. Run the script to execute the CNN with artificial images.

Notes

• Ensure that the necessary dataset files are available and properly formatted before running the scripts.
• Make sure the dependencies are installed correctly and up to date.
• Adjust any relevant paths or configurations within the scripts to match your specific environment and requirements.

Conclusion

By utilizing the provided scripts and following the instructions outlined in this readme, you can explore the potential of fine-tuning the diffusion model and generating artificial images for improving lymphoma classification accuracy. Feel free to experiment with different parameters, architectures, or fine-tuning techniques to further enhance the performance of the models.

If you have any questions or encounter any issues while running the project, please refer to the project documentation or reach out to the project contributors for assistance.