COVID-19 Prediction Models
Our COVID-19 prediction models are composed of a novel coronavirus pneumonia (NCP) recognition
model trained by a large number of COVID-19 patient CT image data sets. It can accurately identify
important clinical markers related to the characteristics of NCP lesions and help the radiologists and
clinicians quickly diagnose and identify COVID-19 patients for early intervention and appropriate
allocation of resources.
The diseases caused by the 2019 novel coronavirus (SARS-CoV-2) are collectively referred to as COVID-
19. These diseases have a high mortality rate, which can cause fever, cough and other flu-like symptoms.
Many affected patients will develop novel coronavirus pneumonia (NCP) and rapidly develop severe
acute respiratory failure. Chest computed tomography (CT) radiography is an important tool for
diagnosing lung diseases including NCP. Our COVID-19 prediction models based on deep learning
algorithms use a large amount of clinical data and CT to establish NCP recognition models, which helps to
distinguish NCP and common influenza or other types of pneumonia (such as viral pneumonia and
bacterial pneumonia) can help radiologists and clinicians quickly diagnose and identify COVID-19
patients, so that clinicians can plan for early monitoring and management of these patients.
SOLUTIONS PLATFORM CAREERS
Please input your keywords...
Lung Lesion Segmentation Model
Lung lesion segmentation model is a segmentation framework based on U-Net deep neural network,
which solves the problem of semantic level and realizes end-to-end segmentation. In addition, this model
is trained with more than 5,000 CT slices from NCP patients and common pneumonia patients. During
the training process, Dice Coefficient and 10-fold cross-validation are used to evaluate and adjust the
model. The accuracy of the final model can reach more than 97%, which can accurately segment the lung
parenchyma part of the original lung CT image and identify the normal area and