Res-Dense Net for 3D Covid Chest CT-Scan Classification
21st International Conference on Image Analysis and Processing , ICIAP 2022
; 13374 LNCS:483-495, 2022.
Article
in English
| Scopus | ID: covidwho-2013962
ABSTRACT
One of the most contentious areas of research in Medical Image Preprocessing is 3D CT-scan. With the rapid spread of COVID-19, the function of CT-scan in properly and swiftly diagnosing the disease has become critical. It has a positive impact on infection prevention. There are many tasks to diagnose the illness through CT-scan images, include COVID-19. In this paper, we propose a method that using a Stacking Deep Neural Network to detect the Covid 19 through the series of 3D CT-scans images. In our method, we experiment with two backbones are DenseNet 121 and ResNet 101. This method achieves a competitive performance on some evaluation metrics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Databases of international organizations
Database:
Scopus
Language:
English
Journal:
21st International Conference on Image Analysis and Processing , ICIAP 2022
Year:
2022
Document Type:
Article
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