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Knee Osteoarthritis Analysis with X-ray Images using CNN

License: MIT License

Python 85.60% Shell 0.52% C++ 0.10% Cuda 3.54% Cython 10.24%
deep-learning biomedical-data-science knee-joints knee-osteoarthritis-analysis kl-grading grading-system x-ray-images cnn-classification yolov2

gradingkneeoa's Introduction

Knee Osteoarthritis Analysis

Pipeline of knee osteoarthritis grading pipeline, which includes knee joints detection and knee OA grading.

klg_pipeline

DetJoint

  • Detecting two knee joints in X-ray images using a customized YOLOv2 model.

ClsKL

  • Classifying the KL grade of detected knee joints with a novel ordinal loss.

Data & Models

Knee joint detection (DetJoint) and KL grading (ClsKL) training/testing datasets, as well as best models, can be downloaded from KneeXrayData, around 7G.

Citation

Please consider cite the paper if you use the code or data for your research.

@article{chen2019fully,
  title={Fully Automatic Knee Osteoarthritis Severity Grading Using Deep Neural Networks with a Novel Ordinal Loss},
  author={Chen, Pingjun and Gao, Linlin and Shi, Xiaoshuang and Allen, Kyle and Yang Lin},
  journal={Computerized Medical Imaging and Graphics},,
  volume={75},
  pages={84--92},
  year={2019},
  doi={https://doi.org/10.1016/j.compmedimag.2019.06.002},
  publisher={Elsevier}  
}

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