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Updated: Oct 10, 2025
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
Lung detection and severity prediction of pneumonia patients based on COVID-19 DET-PRE network
Jiaqiao Zhang1, Yan Yan1, Hongjun Ni2
1School of Mechanical Engineering, Southeast University, Nanjing, China.
Background:
The sudden outbreak of COVID-19 pneumonia has brought a heavy disaster to individuals globally. Facing this new virus, the clinicians have no automatic tools to assess the severity of pneumonia patients.
Methods:
In the current work, a COVID-19 DET-PRE network with two pipelines was proposed. Firstly, the lungs in X-rays were detected and segmented through the improved YOLOv3 Dense network to remove redundant features. Then, the VGG16 classifier was pre-trained on the source domain, and the severity of the disease was predicted on the target domain by means of transfer learning.
Results:
The experiment results demonstrated that the COVID-19 DET-PRE network can effectively detect the lungs from X-rays and accurately predict the severity of the disease. The mean average precisions (mAPs) of lung detection in patients with mild and severe illness were 0.976 and 0.983 respectively. Moreover, the accuracy of severity prediction of COVID-19 pneumonia can reach 86.1%.
Conclusions:
The proposed neural network has high accuracy, which is suitable for the clinical diagnosis of COVID-19 pneumonia.
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