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covidnet_severity.md

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COVIDNet Lung Severity Scoring

COVIDNet-SEV-GEO and COVIDNet-SEV-OPC models takes as input a chest x-ray image of shape (N, 480, 480, 3), where N is the number of batches, and outputs the SARS-CoV-2 severity scores for geographic extent and opacity extent, respectively. COVIDNet-SEV-GEO predicts the geographic severity. Geographic severity is based on the geographic extent score for right and left lung. For each lung: 0 = no involvement; 1 = <25%; 2 = 25-50%; 3 = 50-75%; 4 = >75% involvement, resulting in scores from 0 to 8. COVIDNet-SEV-OPC predicts the opacity severity. Opacity severity is based on the opacity extent score for right and left lung. For each lung: 0 = no opacity; 1 = ground glass opacity; 2 =consolidation; 3 = white-out, resulting in scores from 0 to 6. For detailed description of COVIDNet lung severity scoring methodology, see the paper here.

If using the TF checkpoints, here are some useful tensors:

  • input tensor: input_1:0
  • logit tensor: MLP/dense_1/MatMul:0
  • is_training tensor: keras_learning_phase:0

Steps for inference

DISCLAIMER: Do not use this prediction for self-diagnosis. You should check with your local authorities for the latest advice on seeking medical assistance.

  1. Download the COVIDNet Lung Severity Scoring models from the pretrained models section
  2. Locate both geographic and opacity models and COVID-19 positive chest x-ray image to be inferenced
  3. To predict geographic and opacity severity
python inference_severity.py \
    --weightspath_geo models/COVIDNet-SEV-GEO \
    --weightspath_opc models/COVIDNet-SEV-OPC \
    --metaname model.meta \
    --ckptname model \
    --imagepath assets/ex-covid.jpeg
  1. For more options and information, python inference_severity.py --help