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README.md

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README

Description

This project uses a deep learning model to classify MRI scans into different phases: 'EO', 'IO', 'IPTE', 'LO', and 'PTE'.

Prerequisites

  • You should have Docker installed on your machine to use this model.
  • You will need to download the model:

Steps to Run

  1. Extract the zip file. This will place the project files into a directory on your local machine.

  2. Navigate to the project directory. This is the location where the project files are stored.

  3. Within this directory, you will find two Bash scripts. The first script, build.sh, builds a Docker image from the Dockerfile. The second script, run.sh, runs the Docker image.

  4. Open a terminal window and navigate to the project directory.

  5. Run the following commands to give execute permissions to your scripts:

  6. Execute the build.sh script to build the Docker image:

  7. Once the Docker image has been successfully built, open the run.sh file.

  8. Replace TEST_DIR with the path to your test data directory and TEST_NAME with the name of the test image (in '.jpg' format) you wish to classify.

  9. Save and close the file.

  10. Execute the run.sh script to run the Docker container and make the prediction:

  11. The script will print a prediction for the test image, indicating the phase the MRI scan most likely belongs to along with the associated confidence percentage.

Important Notes

  • Your Dockerfile should be located in the same directory as your Bash scripts.
  • The model file should be located in the directory specified by the MODEL_DIR environment variable.
  • download the model: https://drive.google.com/file/d/1-77OVZFZwxeh-JkJZGC9VLxZx1eamBa1/view?usp=sharing
  • The test image should be located in the directory specified by TEST_DIR.
  • The name of the test image should match TEST_NAME as defined in the run.sh script.
  • Always remember to grant execute permissions to your Bash scripts before running them.
  • Ensure that Docker is installed and running on your system.
  • To modify the MODEL_DIR and MODEL_NAME environment variables, adjust them in the operating system where Docker is running. These variables denote the location and name of the model used for prediction.