COVID-CT-Mask-Net: Prediction of COVID-19 from CT Scans Using Regional Features

Abstract

Abstract We present COVID-CT-Mask-Net model that predicts COVID-19 from CT scans. The model works in two stages: first, it detects the instances of ground glass opacity and consolidation in CT scans, then predicts the condition from the ranked bounding box detections. To develop the solution for the three-class problem (COVID, common pneumonia and control), we used the COVIDx-CT dataset derived from the dataset of CT scans collected by China National Center for Bioinformation. We use about 5% of the training split of COVIDx-CT to train the model, and without any complicated data normalization, balancing and regularization, and training only a small fraction of the model’s parameters, we achieve a 90 . 80 % COVID sensitivity, 91 . 62 % common pneumonia sensitivity and 92 . 10 % normal sensitivity, and an overall accuracy of 91 . 66% on the test data (21182 images), bringing the ratio of test/train data to 7 . 06 , which implies a very high capacity of the model to generalize to new data. We also establish an important result, that ranked regional predictions (bounding boxes with scores) in Mask R-CNN can be used to make accurate predictions of the image class. The full source code, models and pretrained weights are available on https://github.com/AlexTS1980/COVID-CT-Mask-Net .

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