Super-resolution generative adversarial networks (SR-GANs) are a type of deep learning models that can enhance the quality of low-resolution medical images.
Super-resolution generative adversarial networks (SR-GANs) are deep learning models that can enhance the quality of low-resolution medical images. This repository contains code and data related to SR-GANs for medical image processing.
To use the SR-GANs code in this repository, follow the steps below:
Clone the repository to your local machine. Install the required dependencies listed in the Prerequisites section. Run the code with your preferred parameters.
- Python 3.7 or higher
- PyTorch 1.13.0 or higher
- Matplotlib
- Numpy
The blood cell counter is based on YoLo v7, a state-of-the-art object detection algorithm that can accurately detect and classify objects in images. The model is trained on a large dataset of microscopic blood cell images, which includes different types of blood cells such as red blood cells, white blood cells, and platelets. The model is optimized for speed and accuracy, making it suitable for real-time blood cell counting in clinical settings.
If you would like to contribute to this project, please follow the guidelines below:
- Fork the repository and create a new branch for your contribution.
- Make your changes and test them thoroughly.
- Create a pull request with a detailed description of your changes.
- Your contribution will be reviewed by the project maintainers, and any feedback will be provided.
This project is licensed under the MIT License. See the LICENSE file for more details.
For any questions or inquiries, please contact mmasadar@gmail.com or visit my personal website at mahasin.tech.