Improved Residual U-Net for COVID-19 Lung Infection Multi-Class Segmentation in CT Image
2022 International Conference of Advanced Technology in Electronic and Electrical Engineering, ICATEEE 2022
; 2022.
Article
in English
| Scopus | ID: covidwho-2316058
ABSTRACT
COVID-19, the new coronavirus, is a threat to global public health. Today, there is an urgent need for automatic COVID-19 infection detection tools. This work proposes an automatic COVID-19 infection detection system based on CT image segmentation. A deep learning network developed from an improved Residual U-net architecture extracts infected areas from a CT lung image. We tested the system on COVID-19 public CT images. An evaluation using the F1 score, sensitivity, specificity and accuracy proved the effectiveness of the proposed network. Besides, experimental results showed that the proposed network performed well in extracting infection regions so, it can assist experts in COVID-19 infection detection. © 2022 IEEE.
Coronavirus; COVID-19; CT image; Deep Residual U-net Segmentation; Biological organs; Computerized tomography; Deep learning; Health risks; Image enhancement; Image segmentation; Coronaviruses; Detection system; Detection tools; Global public health; Images segmentations; Learning network; Lung infection; Multi-class segmentations
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2022 International Conference of Advanced Technology in Electronic and Electrical Engineering, ICATEEE 2022
Year:
2022
Document Type:
Article
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