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Healthy, Pneumonia, and Covid-19 chest X-ray classification of V7 Labs & CloudFactory Release Annotated X-Ray Dataset

I used the notebook NB.ipynb to transform the ground truth to work for my setting.

It consisted of ~6000 images with labels when I did this project: Healthy, Pneumonia, and COVID-19 In this work I have applied two classification strategies using popular U-Net architecture.

1. Multi-label classification:

In this setting the labels will be [h, p, c], where each value will be either 0 or 1. For example, if any image is healthy, the label would be [1, 0, 0]. If any image has only Pneumonia it will be [0, 1, 0]. In most cases if there is COVID-19 present, there will also be Pneumonia.

2. Binary Classification

  • In this setting, we treat each image as either image with no-covid (label 0), or with covid(label 1) and do binary classification.

How to run?

  • Download the sample datasets.zip from releases(1400 images out of ~6.5k).
  • Extract in the project directory (inside covidxfactory, so it should be covidxfactory/datasets/v7_lab_cf/...)
  • Install pytorch and torchvision from official website
  • pip install easytorch
  • For full datasets, please visit the original source links provided above and use the notebook NB.ipynb to download the complete ~6k images.
python main.py -ph train -b 32 -wm multi -nw 16 -ep 71 -nch 1 -pat 11
python main.py -ph train -b 32 -wm binary -nw 16 -ep 71 -nch 1 -pat 11

10-fold cross validation results for multi-label classification:

SPLIT Precision Recall F1 Accuracy
SPLIT_0.json 0.7627 0.8333 0.7964 0.9644
SPLIT_5.json 0.8654 0.9783 0.9184 0.9876
SPLIT_8.json 0.8605 0.7708 0.8132 0.9737
SPLIT_4.json 0.7544 0.9556 0.8432 0.9752
SPLIT_3.json 0.7551 0.9024 0.8222 0.9752
SPLIT_1.json 0.7903 0.9074 0.8448 0.9721
SPLIT_9.json 0.86 0.86 0.86 0.9783
SPLIT_6.json 0.7458 0.9167 0.8225 0.9706
SPLIT_2.json 0.7719 0.9778 0.8627 0.9783
SPLIT_7.json 0.8113 0.86 0.8349 0.9737
Global 0.7948 0.894 0.8415 0.9749

10-fold cross validation results for binary classification:

SPLIT Precision Recall F1 Accuracy
SSPLIT_0.json 0.7963 0.7963 0.7963 0.9659
SPLIT_5.json 0.8627 0.9565 0.9072 0.9861
SPLIT_8.json 0.8039 0.8542 0.8283 0.9737
SPLIT_4.json 0.7586 0.9778 0.8544 0.9768
SPLIT_3.json 0.8293 0.8293 0.8293 0.9783
SPLIT_1.json 0.8182 0.8333 0.8257 0.9706
SPLIT_9.json 0.746 0.94 0.8318 0.9705
SPLIT_6.json 0.7778 0.875 0.8235 0.9721
SPLIT_2.json 0.8222 0.8222 0.8222 0.9752
SPLIT_7.json 0.8214 0.92 0.8679 0.9783
Global 0.8011 0.8794 0.8384 0.9748

Training: Plot Validation: Plot

References for U-NET

Architecture used, O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in MICCAI, (2015)