Lung CT image segmentation algorithm based on improved U-net structure
5th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2022
; : 93-96, 2022.
Article
in English
| Scopus | ID: covidwho-2281058
ABSTRACT
Accurate segmentation of medical images can help doctors diagnose and treat diseases. In the face of the complex COVID-19 image, this paper proposes an improved U-net network segmentation model, which uses the residual network structure to deepen the network level, and adds the attention module to integrate different receptive field, global, local and spatial features to enhance the detail segmentation effect of the network. For the COVID-19 CT data set, the F1-Score, Accuracy, SE, SP and Precision of the U-Net network are 0.9176, 0.9578, 0.9669, 0.9487 and 0.8574 respectively. Compared with U-Net, our model proposed in this paper increased by 6.43%, 3.36%, 0.85%, 4.78% and 13.11% on F1-Score, Accuracy, SE, SP and Precision, respectively. The automatic and effective segmentation of COVID-19 lung CT image is realized. © 2022 IEEE.
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
5th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2022
Year:
2022
Document Type:
Article
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