Predicts whether a given image is positive for Pneumonia caused by COVID-19 (Classification). Component for Initial Diagnosis.

The model creator thanks Dr. Jianshu Weng, Mr. Najib Ninaba and their organisation, AI Singapore (AISG), for their generous support in providing the infrastructure to train the latest iteration of the model.

Intended Use

Chest Xray DICOM images.

Information on training data

Model was trained via transfer learning with a Resnet 34 model architecture. Approximately 26,000 images were used with weighted resampling to account for class imbalances.
Training dataset size before Resampling:
1. "covid" - 186
2. "opacity" - 5801
3. "nofinding" - 19884
*Note: "nofinding" images include both healthy and non-healthy lungs that do not exhibit opacity

Model performance metrics

Area under the Receiver Operator Characteristic (AUROC) was the chief metric used to determine model performance. It was calculated with a one-vs-all approach.
AUROC for "covid", "opacity", "nofinding" were at 99.97%, 92.64%, and 92.73%, respectively.

Additional Media

additional

Model History

Model available on Arterys.

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Yale-NUS College

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Ayrton San Joaquin