Use deep learning models to automate the identification and labelling of cancer cells in microscopy images, improving diagnostic efficiency and reducing human error.
This project applies artificial intelligence to automate the identification and labelling of cancer cells in microscopy images. Traditional cancer detection relies on manual inspection, which is time-consuming and prone to error. By using deep learning models like convolutional neural networks (CNNs), the project aims to accurately classify cancerous cells based on visual patterns. Trained on annotated datasets, the AI system demonstrates high accuracy, offering potential improvements in diagnostic efficiency and reducing human error.
Provide instructions on how to install and set up the project, such as installing dependencies and preparing the environment.
# Example command to install dependencies (Python)
pip install project-dependencies
# Example command to install dependencies (R)
install.packages("project-dependencies")Provide a basic usage example or minimal code snippet that demonstrates how to use the project.
# Example usage (Python)
import my_project
demo = my_project.example_function()
print(demo)# Example usage (R)
library(my_project)
demo <- example_function()
print(demo)Add detailed information and examples on how to use the project, covering its major features and functions.
# More usage examples (Python)
import my_project
demo = my_project.advanced_function(parameter1='value1')
print(demo)# More usage examples (R)
library(demoProject)
demo <- advanced_function(parameter1 = "value1")
print(demo)Contributions are welcome! If you'd like to contribute, please open an issue or submit a pull request. See the contribution guidelines for more information.
If you have any issues or need help, please open an issue or contact the project maintainers.
This project is licensed under the MIT License.