A Streamlit application in Python with deep learning models that can assess ACL ligament tear along with the grade of tear.

1. CREATE A PYTHON VIRTUAL ENVIRONMENT
2. INSTALL ALL THE REQUIRED DEPENDENCIES USING Requirements.txt FILE TO RUN THE APP.
3. RUN streamlit run app.py TO RUN THE APPLICATION ON YOUR LOCAL SERVER.
4. TO RUN OTHER PARTS OF THE CODE, DOWNLOAD AND EXTRACT BOTH MRNET AND KNEEMRI DATASETS FROM THEIR RESPECTIVE SOURCES INTO Data FOLDER. MRNet Source : https://stanfordmlgroup.github.io/competitions/mrnet/ KneeMRI Source : http:https://www.riteh.uniri.hr/~istajduh/projects/kneeMRI/
5. RUN Jupyter Notebooks in the sequence a) Preprocess and augment dataset, b) Train models (from scratch and transfer learning)

This repository, Knee-Ligament-Tear-Assessment, may contain images and other media that are subject to copyright protection. These materials are used for educational, illustrative, or informational purposes within the scope of fair use, fair dealing, or similar copyright exceptions and limitations.

The copyright and intellectual property rights to these images and media remain with their respective owners. This repository does not claim ownership or seek to infringe upon those rights.

If you are the copyright owner of any content included in this repository and believe that its use here constitutes copyright infringement, please contact us immediately so that we can address your concerns. We are committed to resolving any copyright issues promptly.

Contact Information:

• Jeevan Pawar (mailto:[email protected])
• jeevan6996 (https://github.com/jeevan6996)

We respect the rights of copyright owners and are dedicated to complying with all applicable copyright laws and regulations. Thank you for your understanding and cooperation.

Knee-Ligament-Tear-Assessment is provided as-is, without warranties of any kind. You assume all responsibility for the use and adherence to copyright laws when accessing or using the content in this repository.